Automated cell location estimation and validation

ABSTRACT

Locations of cells of a communication network can be estimated, determined, and validated. Cell location component (CLC) can analyze timing advance (TA) measurement data and/or location data associated with devices associated with a base station associated with one or more cells. CLC can estimate a first location of the base station, based on the TA measurement data and/or location data, to facilitate estimating the location of an associated cell. CLC can validate the estimated cell location or recorded cell location of the cell (recorded in a cell location pool) based on analysis of estimated cell location, recorded cell location, TA measurement data, and/or location data, and, based on the validation, can tag the cell location determination as accurate, acceptable, bad, or uncertain. CLC can request additional monitoring of a cell location determination tagged as uncertain, or investigation of a cell location determination tagged as bad.

TECHNICAL FIELD

This disclosure relates generally to electronic communications, e.g., toautomated cell location estimation and validation.

BACKGROUND

A node, such as a base station, of a communication network can beassociated with one or more cells. For instance, a node can beassociated with a single cell or multiple cells can be co-located with asame node at a given location. Base stations and cells associated withbase stations are added to communication networks, removed fromcommunication networks, or moved to different locations withincommunication networks on a regular basis. It is expected that thousandsof new cells will be added to the communication networks each year.

The above-described description is merely intended to provide acontextual overview regarding electronic communications, and is notintended to be exhaustive.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates a block diagram of an example system that canestimate a location of a node to facilitate estimating, determining,and/or validating a location of a cell associated with the node, inaccordance with various aspects and embodiments of the disclosed subjectmatter.

FIG. 2 depicts a block diagram of an example cell location estimationand validation process, in accordance with various aspects andembodiments of the disclosed subject matter.

FIG. 3 depicts a diagram of an example communication device distributionin relation to a node associated with one or more cells, in accordancewith various aspects and embodiments of the disclosed subject matter.

FIG. 4 illustrates a diagram of an example communication device pairdistribution in relation to a node associated with one or more cells, inaccordance with various aspects and embodiments of the disclosed subjectmatter.

FIG. 5 depicts a diagram of an example distance difference (DD) of afirst distance between a potential location (e.g., estimated location orrecorded location) of a cell and a location of a communication deviceand a second distance that can be a measurement distance between a truelocation of the cell and the communication device, in accordance withvarious aspects and embodiments of the disclosed subject matter.

FIG. 6 presents a diagram of an example map plot that can include a UEtraffic heatmap, an estimated location of a cell, and a recordedlocation of the cell that are plotted on the map, in accordance withvarious aspects and embodiments of the disclosed subject matter.

FIG. 7 illustrates a block diagram of the example cell locationcomponent, in accordance with various aspects and embodiments of thedisclosed subject matter.

FIG. 8 depicts a block diagram of an example communication deviceoperable to engage in a system architecture that facilitates wirelesscommunications according to one or more embodiments described herein.

FIG. 9 illustrates a flow chart of an example method that can estimate alocation of a node to facilitate estimating a location of a cellassociated with the node, in accordance with various aspects andembodiments of the disclosed subject matter.

FIG. 10 depicts a flow chart of another example method that can estimatea location of a node to facilitate estimating a location of a cellassociated with the node, in accordance with various aspects andembodiments of the disclosed subject matter.

FIG. 11 presents a flow chart of an example method that can determinedistance differences between a recorded or estimated location of a celland respective locations of respective communication devices of a groupof communication devices associated with the cell, to facilitatedetermining a validation status of the recorded location and/orestimated location of the cell, in accordance with various aspects andembodiments of the disclosed subject matter.

FIG. 12 illustrates a flow chart of an example method that can determinea distance between a recorded location of a cell and an estimatedlocation of the cell to facilitate determining a validation status ofthe recorded location and/or estimated location of the cell, inaccordance with various aspects and embodiments of the disclosed subjectmatter.

FIG. 13 illustrates a flow chart of an example method that can determinea validation status of a recorded location and/or an estimated locationof a cell based at least in part on the distance between the recordedlocation and the estimated location, and/or distance differences betweenthe recorded and/or estimated location of the cell and respectivelocations of respective communication devices of a group ofcommunication devices associated with the cell, to facilitatedetermining a validation status of the recorded location and/orestimated location of the cell, in accordance with various aspects andembodiments of the disclosed subject matter.

FIG. 14 is a schematic block diagram illustrating a suitable computingenvironment in which the various embodiments of the embodimentsdescribed herein can be implemented.

DETAILED DESCRIPTION

Various aspects of the disclosed subject matter are now described withreference to the drawings, wherein like reference numerals are used torefer to like elements throughout. In the following description, forpurposes of explanation, numerous specific details are set forth inorder to provide a thorough understanding of one or more aspects. It maybe evident, however, that such aspect(s) may be practiced without thesespecific details. In other instances, well-known structures and devicesare shown in block diagram form in order to facilitate describing one ormore aspects.

Discussed herein are various aspects and embodiments that relate toestimating locations of cells and validating cell locations (e.g.,estimated cell locations and/or recorded cell locations from datasources) of cells of a communication network. The disclosed subjectmatter can enhance (e.g., improve or optimize) the accuracy of celllocation information for service providers, which can enhance design andefficiency of the communication network, enhance performance of thecommunication network, enhance the user experience with regard tocommunications via the communication network, reduced waste in capitalinvestment (e.g., financial costs) and resource usage, enhance E911operations (e.g., enhance the accuracy of determining locations ofmobile communication devices in response to mobility E911 calls).

The various aspects described herein can relate to new radio, which canbe deployed as a standalone radio access technology or as anon-standalone radio access technology assisted by another radio accesstechnology, such as Long Term Evolution (LTE), for example. It should benoted that although various aspects and embodiments have been describedherein in the context of 5G, Universal Mobile Telecommunications System(UMTS), and/or Long Term Evolution (LTE), or other next generationnetworks, the disclosed aspects are not limited to 5G, a UMTSimplementation, and/or an LTE implementation as the techniques can alsobe applied in 2G, 3G, 4G, or LTE systems. For example, aspects orfeatures of the disclosed embodiments can be exploited in substantiallyany wireless communication technology. Such wireless communicationtechnologies can include UMTS, Code Division Multiple Access (CDMA),Wi-Fi, Worldwide Interoperability for Microwave Access (WiMAX), GeneralPacket Radio Service (GPRS), Enhanced GPRS, Third Generation PartnershipProject (3GPP), LTE, Third Generation Partnership Project 2 (3GPP2)Ultra Mobile Broadband (UMB), High Speed Packet Access (HSPA), EvolvedHigh Speed Packet Access (HSPA+), High-Speed Downlink Packet Access(HSDPA), High-Speed Uplink Packet Access (HSUPA), Zigbee, or anotherIEEE 802.XX technology. Additionally, substantially all aspectsdisclosed herein can be exploited in legacy telecommunicationtechnologies. Further, the various aspects can be utilized with anyRadio Access Technology (RAT) or multi-RAT system where the mobiledevice operates using multiple carriers (e.g., LTE Frequency DivisionDuplexing (FDD)/Time-Division Duplexing (TDD), Wideband Code DivisionMultiplexing Access (WCMDA)/HSPA, Global System for MobileCommunications (GSM)/GSM EDGE Radio Access Network (GERAN), Wi Fi,Wireless Local Area Network (WLAN), WiMax, CDMA2000, and so on).

As used herein, “5G” can also be referred to as New Radio (NR) access.Accordingly, systems, methods, and/or machine-readable storage media forreducing interference on reference signals from other co-channelreference signals, and improving the channel estimation performance forCSI estimation and data detection, in 5G systems, and other nextgeneration systems, can be desired. As used herein, one or more aspectsof a 5G network can comprise, but is not limited to, data rates ofseveral tens of megabits per second (Mbps) supported for tens ofthousands of users; at least one gigabit per second (Gbps) that can beoffered simultaneously to tens of users (e.g., tens of workers on thesame office floor); several hundreds of thousands of simultaneousconnections supported for massive sensor deployments; spectralefficiency that can be significantly enhanced compared to 4G;improvement in coverage relative to 4G; signaling efficiency that can beenhanced compared to 4G; and/or latency that can be significantlyreduced compared to LTE.

Multiple Input, Multiple Output (MIMO) technology can be employed incommunication networks, wherein MIMO technology can be an advancedantenna technique utilized to improve spectral efficiency and, thereby,boost overall system capacity. Spectral efficiency (also referred to asspectrum efficiency or bandwidth efficiency) refers to an informationrate that can be transmitted over a given bandwidth in a communicationsystem.

For MIMO, a notation (M×N) can be utilized to represent the MIMOconfiguration in terms of a number of transmit antennas (M) and a numberof receive antennas (N) on one end of the transmission system. Examplesof MIMO configurations used for various technologies can include: (2×1),(1×2), (2×2), (4×2), (8×2) and (2×4), (4×4), (8×4). The configurationsrepresented by (2×1) and (1×2) can be special cases of MIMO known astransmit and receive diversity.

In some cases, MIMO systems can significantly increase the data carryingcapacity of wireless communications systems. Further, MIMO can be usedfor achieving diversity gain, which refers to an increase insignal-to-interference ratio due to a diversity scheme and, thus, canrepresent how much the transmission power can be reduced when thediversity scheme is introduced, without a corresponding performanceloss. MIMO also can be used to achieve spatial multiplexing gain, whichcan be realized when a communications system is transmitting differentstreams of data from the same radio resource in separate spatialdimensions (e.g., data is sent/received over multiple channels, linkedto different pilot frequencies, over multiple antennas). Spatialmultiplexing gain can result in capacity gain without the need foradditional power or bandwidth. In addition, MIMO can be utilized torealize beamforming gain. Due to the benefits achieved, MIMO can be anintegral part of the third generation wireless system and the fourthgeneration wireless system. In addition, 5G systems also will employmassive MIMO systems (e.g., hundreds of antennas at the transmitter sideand receiver side). Typically, with a (N_(t), N_(r)), where N_(t)denotes the number of transmit antennas and N_(r) denotes the number ofreceive antennas, the peak data rate can multiple with a factor of N_(t)over single antenna systems in a rich scattering environment.

Nodes, such as base stations, can be associated with one or more cellsthat can be distributed at various locations in a communication network.A node can be associated with a single cell, or multiple cells can beco-located with a same node at a given location. Base stations and cellsassociated with base stations are added to or removed from communicationnetworks, or are moved to different locations within communicationnetworks, on a regular basis. It is expected that thousands of new cellsites will be added to the communication networks each year.

It can be desirable to know accurate locations of cells for a variety ofreasons. The locations of cells can be used to facilitate determininglocations of communication devices (e.g., cell or smart phones)associated with cells, for example, when it is desired to know thelocations of such communication devices for emergency purposes, such asduring E911 dispatch operations, or for other desired reasons. Asanother example, it can be desirable to accurately know locations ofcells for radio access network (RAN) planning, network optimization,and/or ongoing operations of communication networks.

However, cell location information often can be inaccurate due toerroneous information or incomplete information inputted to a celllocation database when people manually input information into the celllocation database, inaccurate or incomplete information received fromcertain cells that can self report their locations, or other sources oferror in cell locations. Inaccurate cell locations can result inundesirable problems with regard to, for example, E911 dispatchoperations and RAN planning.

For instance, with regard to E911 calls, while mobile communicationdevices often can provide location information (e.g., GPS locationinformation) regarding their locations, sometimes such locationinformation of mobile communication devices may not be available. Atleast in such instances, the communication network, via the cells of thenetwork, can be utilized to facilitate determining locations of mobilecommunication devices. During an E911 dispatch operation, a mobilityE911 call from a mobile communication device can route to a servingpublic safety answering point (PSAP) based on the location of the cellsite that handles the E911 call. For example, for Phase 2 capable PSAPs,the evolved serving mobile location center (E-SMLC) can return acalculated location for the E911 caller. Inaccuracies in either the PSAPcall routing (e.g., due to error or other inaccuracy in the location ofthe cell that handles the E911 call) or reported caller location cancause an undesirable delay in providing desired emergency assistancefrom emergency assistance personnel (e.g., law enforcement, medicalpersonnel (e.g., emergency medical services (EMS)), or other emergencypersonnel).

As another example, with regard to RAN planning, locations of existingcell sites, along with demand forecast and radio frequency (RF)propagation, can be key inputs for the RAN capacity planning process.Errors or other inaccuracies in cell site locations can lead toerroneous and inefficient RAN planning and design of the communicationnetwork, which can result in wasted capital investment in thecommunication network and/or undesirable (e.g., inefficient, suboptimal,and/or poor) communication network performance.

As new communication network technology (e.g., 5G NR millimeter waves(5G NR mmWave) or other new communication network technology) isdeployed in the communication network, thousands of new cells at newcell locations (e.g., cell sites) are expected to be added to thecommunication network each year, for the foreseeable future. Thevalidation of the accuracy of locations of cells can be a significantproblem for which it can be desirable for a network operator or providerto address to facilitate improving E911 dispatch operations, RANplanning, and communication network performance.

Traditional techniques for determining and validating cell locations canbe undesirably inefficient, inaccurate or insufficiently accurate, orotherwise undesirable. For instance, manual investigation can be onetraditional method to determine, correct errors in, or verify celllocations. Manual investigation can involve dispatching a person (e.g.,an engineer or technician) to visit a site of a cell to determine orverify the location of the cell site. This can be inefficient and/or canlead to inaccuracies with regard to cell locations because it can beundesirably labor intensive to have such person determine and verify thecell location, since the person will have to take the time to travel toeach of many cell locations to determine and verify the respectivelocations of those cell location, it can be undesirably inaccuratebecause such person can inadvertently input erroneous or incompleteinformation to the cell location database, and/or it can be difficult toscale as there can be a very large number of cells that are to havetheir locations determined and verified.

Another traditional technique for determining and validating celllocations can be image-based validation Image-based validation caninvolve checking cell site locations via satellite or street view images(e.g., geographical map images) of objects, such as base stationsassociated with cells. The image-based validation can be conducted byhumans, which can be undesirably labor intensive and/or can result ininaccurate results, or can utilize artificial intelligence (AI) basedimage recognition. Overall, the image-based validation method can behighly depending on the image quality and reliability of the images usedto determine and validate the locations of the cells. For example,satellite or street view images usually can be refreshed once everyseveral months or years. However, new cell sites added to thecommunication network often may not be captured in the satellite orstreet view images. Further, the image-based validation method often canonly provide insufficient or incomplete information regarding the cells.For instance, from a satellite or street view image, the image-basedvalidation method typically only can tell if there is a cell at alocation in the image, but cannot confirm other cell attributes, suchas, for example, RF band and an equipment vendor, of the cell. Inaddition, AI-based image recognition can utilize a significant andundesirable amount of resources to procure the images and train orimplement the AI image recognition models.

Still another traditional technique for determining and validating celllocations can involve utilizing inference based on drive tests. Therehave been attempts to utilize drive test data to approximately estimatecell locations. However, given the undesirably limited measurement andrelatively large time intervals (e.g., several months or quarters)between subsequent drive tests, this drive-test inference technique canbe undesirable (e.g., inadequate, inefficient, or inaccurate), as thistechnique may not be able to capture, or at least adequately capture,deployed cell sites, including newly deployed cell sites. For example,the estimation errors for cell locations can be on the order of 300meters, which can be undesirable (e.g., unsatisfactory or unsuitable)for the purpose of cell location accuracy and validation.

To that end, techniques for estimating, determining, and validatinglocations of cells of a communication network are presented. A celllocation component (CLC) can analyze timing advance (TA) measurementdata and/or location data (e.g., global positioning system (GPS)location data, assisted or augmented GPS (AGPS) location data, and/orInternet of things (IoT) geolocation data) associated with communicationdevices that can be associated with a base station, which can compriseor be associated with one or more cells (e.g., respective communicationdevices can be associated with (e.g., served by, communicativelyconnected to, observed by, or otherwise associated with respectivecells). The CLC can include an estimator component that can estimate alocation of the base station (e.g., a network node associated with theone or more cells), based at least in part on the respective TAmeasurement data and/or the respective location data associated with therespective communication devices, in accordance with defined celllocation management criteria. In some embodiments, the estimatorcomponent can utilize machine learning (ML) techniques and algorithms tofacilitate estimating the location of the base station.

In some embodiments, the estimator component can determine whether thereis a sufficient number (e.g., a defined threshold number) ofcommunications at or within a defined threshold distance of the basestation, based at least in part on the respective TA measurement dataassociated with the respective communication devices. If the estimatorcomponent determines that there is a sufficient number of communicationsat or within the defined threshold distance of the base station, theestimator component can determine that a smallTA algorithm can beutilized to estimate the location of the base station, in accordancewith the defined cell location management criteria. In such instance,employing the smallTA algorithm, the estimator component can estimatethe location of the base station based at least in part on (e.g., as afunction of) the location data (e.g., AGPS or GPS location data) ofthose communication devices that are determined to be at or within thedefined threshold distance of the base station. For instance, theestimator component can estimate the location of the base station basedat least in part on the median of the communication device locations asdetermined from the location data of those communication devices.

In certain embodiments, if, instead, the estimator component determinesthat there is not a sufficient number of communications at or within thedefined threshold distance of the base station, the estimator componentcan determine that a linear regression algorithm can be utilized toestimate the location of the base station. The estimator component canapply the linear regression algorithm, with respect to respective pairsof locations of communication devices, and can estimate the location ofthe base station based at least in part on the respective TA measurementdata and/or the respective location data associated with the respectivecommunication devices, and the application of the linear regressionalgorithm, in accordance with the defined cell location managementcriteria. The CLC can estimate the location of a cell associated withthe base station based at least in part on the estimated location of thebase station (e.g., as determined using the smallTA algorithm or thelinear regression algorithm). For instance, the CLC can estimate thelocation of the cell as being the estimated location of the associatedbase station.

The CLC also can include a validator component that, for each cell, canvalidate a potential location of a cell (e.g., the estimated celllocation, or a recorded cell location of the cell, as obtained from adata source and recorded in a cell location pool) based at least in parton an analysis of the estimated cell location, the recorded celllocation, and/or the TA measurement data and/or location data associatedwith the communication devices (e.g., communication devices associatedwith the cell), in accordance with the defined cell location managementcriteria. In accordance with various embodiments, the validatorcomponent can utilize a validation algorithm (e.g., distance differencevalidation algorithm) and/or a set of validation rules to facilitatevalidating the potential cell location, including determining anaccuracy level that can indicate how accurate the potential celllocation is. Based at least in part on the results of the validation,the CLC can tag the potential cell location as being accurate,acceptable, bad, or uncertain, for example.

If the CLC determines that a potential cell location (e.g., estimatedcell location, or recorded cell location) of a cell is accurate, the CLCcan lock the cell location with a “good” or “accurate” tag to facilitatepreventing undesired (e.g., unwanted, inadvertent, or unexpected)changes to the cell location, and can store the cell locationinformation for the cell (e.g., updated cell location information),including the lock and tag information, in the cell location pool. Ifthe CLC determines that a potential cell location is bad (e.g.,unacceptably inaccurate), the CLC can tag the potential cell location asbeing bad, can store the cell location information for the cell (e.g.,updated cell location information), including the bad tag, in the celllocation pool, and can initiate a cell location investigation tofacilitate having a manual investigation (e.g., manual investigation onmap, or a physical visit to the cell) performed to determine the celllocation. If the CLC determines that a potential cell location isuncertain, the CLC can tag the potential cell location with an uncertaintag and can initiate a cell monitoring request to request that furtherdata collection (e.g., TA measurement data, AGPS or GPS location data, .. . ) be performed for a desired period of time (e.g., a longer periodof time) for further evaluation by the CLC to facilitate desirably(e.g., accurately) determining the location of the cell.

These and other aspects and embodiments of the disclosed subject matterwill now be described with respect to the drawings.

Referring now to the drawings, FIG. 1 illustrates a block diagram of anexample system 100 that can estimate a location of a node (e.g., basestation) to facilitate estimating, determining, and/or validating alocation of a cell associated with the node, in accordance with variousaspects and embodiments of the disclosed subject matter. The system 100can include a communication network 102 that can comprise a mobilitycore network (e.g., a wireless communication network) and/or a packetdata network (e.g., an Internet Protocol (IP)-based network, such as theInternet and/or intranet) that can be associated with the mobility corenetwork.

The mobility core network of the communication network 102 can operateto enable wireless communication between communication devices and/orbetween a communication device and the communication network 102. Thecommunication network 102 can include various components, such asnetwork (NW) nodes, e.g., radio network nodes) that can be part of thecommunication network 102 to facilitate communication of informationbetween devices (e.g., communication devices) that can be associatedwith (e.g., communicatively connected to) the communication network 102.In some embodiments, the communication network 102 can employ MIMOtechnology to facilitate data communications between devices (e.g.,network devices, communication devices, . . . ) associated with thecommunication network 102.

As used herein, the terms “network node,” “network node component,” and“network component” can be interchangeable with (or include) a network,a network controller, or any number of other network components.Further, as utilized herein, the non-limiting term radio network node,or network node can be used herein to refer to any type of network nodeserving communications devices and/or connected to other network nodes,network elements, or another network node from which the communicationsdevices can receive a radio signal. In cellular radio access networks(e.g., universal mobile telecommunications system (UMTS) networks),network nodes can be referred to as base transceiver stations (BTS),radio base station, radio network nodes, base stations, NodeB, eNodeB(e.g., evolved NodeB), and so on. In 5G terminology, the network nodescan be referred to as gNodeB (e.g., gNB) devices. Network nodes also caninclude multiple antennas for performing various transmission operations(e.g., MIMO operations). A network node can comprise a cabinet and otherprotected enclosures, an antenna mast, and actual antennas. Networknodes can serve several cells, also called sectors, depending on theconfiguration and type of antenna. Network nodes can be, for example,Node B devices, base station (BS) devices, access point (AP) devices,TRPs, and radio access network (RAN) devices. Other examples of networknodes can include multi-standard radio (MSR) nodes, comprising: an MSRBS, a gNodeB, an eNodeB, a network controller, a radio networkcontroller (RNC), a base station controller (BSC), a relay, a donor nodecontrolling relay, a BTS, an AP, a transmission point, a transmissionnode, a Remote Radio Unit (RRU), a Remote Radio Head (RRH), nodes indistributed antenna system (DAS), and the like. In accordance withvarious embodiments, a network node can be, can include, or can beassociated with (e.g., communicatively connected to) a network device ofthe communication network 102.

At given times, one or more communication devices, such as, for example,communication device 104, communication device 106, and communicationdevice 108, can connect or attempt to connect to the communicationnetwork 102 to communicate with other communication devices associatedwith the communication network 102. A communication device (e.g., 104,106, or 108, . . . ) also can be referred to as, for example, a device,a mobile device, or a mobile communication device. The termcommunication device can be interchangeable with (or include) a UE orother terminology. A communication device (or UE, device, . . . ) canrefer to any type of wireless device that can communicate with a radionetwork node in a cellular or mobile communication system. Examples ofcommunication devices can include, but are not limited to, a device todevice (D2D) UE, a machine type UE or a UE capable of machine to machine(M2M) communication, a Personal Digital Assistant (PDA), a tablet or pad(e.g., an electronic tablet or pad), an electronic notebook, a mobileterminal, a cellular and/or smart phone, a computer (e.g., a laptopembedded equipment (LEE), a laptop mounted equipment (LME), or othertype of computer), a smart meter (e.g., a smart utility meter), a targetdevice, devices and/or sensors that can monitor or sense conditions(e.g., health-related devices or sensors, such as heart monitors, bloodpressure monitors, blood sugar monitors, health emergency detectionand/or notification devices, . . . ), a broadband communication device(e.g., a wireless, mobile, and/or residential broadband communicationdevice, transceiver, gateway, and/or router), a dongle (e.g., aUniversal Serial Bus (USB) dongle), an electronic gaming device,electronic eyeglasses, headwear, or bodywear (e.g., electronic or smarteyeglasses, headwear (e.g., augmented reality (AR) or virtual reality(VR) headset), or bodywear (e.g., electronic or smart watch) havingwireless communication functionality), a music or media player, speakers(e.g., powered speakers having wireless communication functionality), anappliance (e.g., a toaster, a coffee maker, a refrigerator, or an oven,. . . , having wireless communication functionality), a set-top box, anIP television (IPTV), a device associated or integrated with a vehicle(e.g., automobile, airplane, bus, train, or ship, . . . ), a virtualassistant (VA) device, a drone, a home or building automation device(e.g., security device, climate control device, lighting control device,. . . ), an industrial or manufacturing related device, a farming orlivestock ranch related device, and/or any other type of communicationdevices (e.g., other types of IoTs).

It is noted that the various aspects of the disclosed subject matterdescribed herein can be applicable to single carrier as well as tomulticarrier (MC) or carrier aggregation (CA) operation of thecommunication device. The term carrier aggregation (CA) also can bereferred to (e.g., interchangeably called) “multi-carrier system,”“multi-cell operation,” “multi-carrier operation,” “multi-carrier”transmission and/or reception. In addition, the various aspectsdiscussed can be applied for Multi RAB (radio bearers) on some carriers(e.g., data plus speech can be simultaneously scheduled).

It is to be appreciated and understood that the terms element (e.g.,element in connection with an antenna), elements, and antenna ports alsocan be used interchangeably, but can carry the same meaning, in thissubject disclosure. In some embodiments, more than a single antennaelement can be mapped to a single antenna port.

As disclosed, the mobility core network of the communication network 102can include various network components or devices, which can include oneor more base stations, such as, for example, base station 110. Forinstance, the mobility core network can include one or more radio accessnetworks (RANs) (not explicitly shown in FIG. 1), wherein each RAN caninclude one or more base stations (e.g., access points (APs)), such as,for example base station 110. Each base station (e.g., base station 110)can serve communication devices (e.g., communication devices 104, 106,and/or 108) located in respective coverage areas served by respectivebase stations in the mobility core network of the communication network102. The respective base stations can be associated with one or moresectors (not shown), wherein respective sectors can comprise respectivecells. For instance, the base station 110 can comprise or be associatedwith one or more cells, such as, for example, cell 112, cell 114, and/orcell 116. The cells can have respective coverage areas that can form thecoverage area covered by the one or more sectors. The respectivecommunication devices can be communicatively connected to thecommunication network 102 via respective wireless or wirelinecommunication connections with one or more of the respective cells.

In some embodiments, a RAN can be an open-RAN (O-RAN) that can employ anopen interface that can support interoperability of devices (e.g.,network devices) from different entities (e.g., vendors). The O-RAN canbuild or establish wireless connections through virtualization. Incertain embodiments, the O-RAN can utilize a common platform that canreduce reliance on proprietary platforms of service providers. The O-RANalso can employ standardized interfaces and application programminginterfaces (APIs) to facilitate open source implementation of the O-RAN.In certain embodiments, the RAN can be a cloud-RAN (C-RAN) that can belocated in or associated with a cloud computing environment, which caninclude various cloud network components of the communication network102.

It is to be appreciated and understood that, while various aspects andembodiments of the disclosed subject matter are described herein withregard to 5G and other next generation communication networks, thetechniques of the disclosed subject matter described herein can beutilized (e.g., applied to), in same or similar form, to 4Gcommunication networks, and the disclosed subject matter includes allsuch aspects and embodiments relating to implementation of thetechniques of the disclosed subject matter to 4G communication networks.

As disclosed herein, it can be desirable to accurately know thelocations of cells (e.g., cell 112, cell 114, cell 116) associated withthe communication network 102. There are various data sources that cancontain and provide, with varying degrees of reliability, locationinformation of the locations of cells at different levels ofgranularity, such as, for example, transmitter location, node location(e.g., eNodeB location), or UE-specific identifier (USID) location, etc.Some example data sources can include ATOLL and CSSNG. Such locationinformation regarding cell locations usually can be collected bydifferent vendors or carriers. The system 100 can include a celllocation pool component 118 that can receive, obtain, or collect thelocation information regarding the locations of the cells from thevarious data sources, for example, in a centralized place form or place(e.g., a data store or database of the cell location pool component118). The cell location pool component 118 can be associated with (e.g.,communicatively connected to) the communication network 102 (asdepicted) or part of the communication network 102.

The cell location pool component 118 can share (e.g., provide or makeavailable) the location information regarding the cell locations withdownstream applications for cell location validation or other desireduses. Different data sources typically can use different cell keys(e.g., cell tower ID (CellID), cell global identification (CGI), E-UTRANCGI (ECGI), cell name (cellname), global cell identity (GCI), or othertype of cell key). The cell location pool component 118 can unify thecell key for a cell, which can be used (e.g., can relatively easily beused) to join with network measurement reported cell key.

In accordance with various embodiments, to facilitate improving theaccuracy of cell locations of cells, the system 100 can include a celllocation component (CLC) 120 that can employ an estimator component 122that can desirably estimate or determine respective locations ofrespective cells (e.g., cell 112, cell 114, cell 116) associated withrespective base stations (e.g., base station 110) of the communicationnetwork 102, based at least in part on respective timing advance (TA)measurement data and/or respective location data (e.g., AGPS locationdata, GPS location data, and/or IoT geolocation data) associated withrespective communication devices (e.g., communication devices 104, 106,and/or 108) associated with the respective base stations. In someembodiments, the estimator component 122 can employ machine learningtechniques and algorithms to facilitate estimating or determiningrespective locations of respective nodes (e.g., base station 110) tofacilitate estimating or determining respective locations of cellsassociated with the respective nodes, in accordance with defined celllocation management criteria.

The CLC 120 also can include a validator component 124 that can validateor verify the accuracy of cell locations. For instance, for each cell(e.g., cell 112, cell 114, and cell 116), the validator component 124can validate or verify the accuracy of the estimated location of thecell or a recorded location of the cell, wherein the validator component124 can obtain information relating to (e.g., indicating or identifying)the recorded location of the cell from the cell location pool component118. In some embodiments, for each cell (e.g., cell 112, cell 114, andcell 116), the validator component 124 can perform a cross validation ofthe estimated location of the cell and (e.g., vis-à-vis, or in relationto) one or more recorded locations of the cell obtained from one or moredata sources (e.g., and stored in the cell location pool component 118),as more fully described herein. The validator component 124 can employvalidation techniques and algorithms, and/or can employ machine learningtechniques and algorithms, to facilitate validating or verifying theaccuracy of the estimated location of the cell or recorded location ofthe cell.

Based at least in part on the level of accuracy of the estimatedlocation or recorded location of the cell (e.g., cell 112, cell 114, orcell 116), the validator component 124 can tag the estimated location orrecorded location of the cell as being accurate, acceptable, bad (e.g.,unacceptable), or uncertain, or with other desired accuracy levelidentifiers. The CLC 120 can provide feedback information (e.g.,validation results from the validator component 124) to the celllocation pool component 118, as or when desired (e.g., on a regular orperiodic basis, dynamically, based on random cell location checks, or asotherwise desired), to facilitate updating the cell location poolcomponent 118 with improved (e.g., more accurate or corrected) celllocation results and/or with the most reliable data source forrespective cells (e.g., cell 112, cell 114, or cell 116). For instance,if the CLC 120 determines improved (e.g., more accurate or corrected)cell location information for a cell (e.g., cell 112, cell 114, or cell116), and/or determines a particular cell location associated with(e.g., provided by) a particular data source has a higher accuracy levelthan other cell location information associated with other data sources,the CLC 120 can update the cell location information for the cell in thecell location pool component 118 with the improved cell location resultsand/or with the most reliable data source. The cell location poolcomponent 118 can share the improved cell location information, obtainedfrom the improved cell location results and/or with the most reliabledata source, with other downstream applications.

In some embodiments, if the CLC 120 determines that a potential celllocation (e.g., estimated cell location, or recorded cell location) of acell (e.g., cell 112, cell 114, or cell 116) is accurate, the CLC 120can lock the cell location with a “good” or “accurate” tag to facilitatepreventing undesired (e.g., unwanted, inadvertent, or unexpected)changes to the cell location information of the cell that is stored inthe cell location pool of the cell location pool component 118, and canstore the cell location information (e.g., updated cell locationinformation) for the cell, including the lock and tag information, inthe cell location pool of the cell location pool component 118. If theCLC 120 determines that a potential cell location is bad (e.g.,unacceptably inaccurate), the CLC 120 can tag the potential celllocation as being bad, can store the cell location information (e.g.,updated cell location information) for the cell, including the bad tag,in the cell location pool of the cell location pool component 118, andcan initiate a cell location investigation to facilitate having a manualinvestigation (e.g., manual investigation on map, or a physical visit tothe cell) performed to determine the cell location, as more fullydescribed herein.

In certain embodiments, if the CLC 120 determines that a potential celllocation (e.g., estimated cell location, or recorded cell location) of acell (e.g., cell 112, cell 114, or cell 116) is uncertain, the CLC 120can tag the potential cell location with an uncertain tag and caninitiate a cell monitoring request to request that further datacollection (e.g., TA measurement data, AGPS or GPS location data, and/orIoT geolocation data, . . . ) be performed for a desired period of time(e.g., a longer period of time). The CLC 120 can evaluate the additionalcollected data with respect to the potential cell location, estimate thecell location based at least in part on the additional collected data,and perform cell validation on the estimated cell location and/or arecorded cell location(s) for the cell, to facilitate desirably (e.g.,accurately) determining the location of the cell, in accordance with thedefined cell location management criteria, as more fully describedherein.

Other aspects and embodiments of the disclosed subject matter will bedescribed with regard to the other figures (and/or FIG. 1).

Referring to FIG. 2 (along with FIG. 1), FIG. 2 depicts a block diagramof an example cell location estimation and validation process 200, inaccordance with various aspects and embodiments of the disclosed subjectmatter. As indicated at reference numeral 202 of the cell locationestimation and validation process 200, the CLC 120 can perform datacollection to collect desired (e.g., relevant) data, including, forexample, respective location data relating to respective communicationdevices (e.g., communication devices 104, 106, and/or 108) associatedwith respective base stations (e.g., base station 110) of thecommunication network 102, and respective call trace records associatedwith the respective communication devices. Location data associated witha communication device can include, for example, location data (e.g.,AGPS or GPS location data, or location data from drive tests, . . . )reported actively by the communication device, or, for a stationarycommunication device (e.g., a fixed or stationary IoT device), locationdata (e.g., IoT geolocation data, AGPS or GPS location data, Wi-Filocation positioning data, or long range wide area networks (LoRa WAN),. . . ). Call trace records can include trace measurement data or TAmeasurement data and/or real time difference (RTD) data from which thedistance between a communication device (e.g., communication device 104)and cell locations of cells (e.g., cell 112, cell 114, or cell 116) canbe derived or determined. With some communication devices, there can beboth location data and TA measurement data, whereas, with othercommunication devices there may be either location data or TAmeasurement data.

The CLC 120 can combine or join the respective location data associatedwith respective communication devices with respective TA measurementdata and/or RTD data based at least in part on respective deviceidentifiers (e.g., UE IDs) of the respective communication devices andrespective timestamps associated with the respective location dataand/or respective TA measurement or RTD data. The CLC 120 also can groupthe respective location data, respective TA measurement data, and/orrespective RTD data associated with respective communication devices(e.g., communication devices 104, 106, and/or 108) by serving node(e.g., each base station, such as base station 110). As disclosed, eachserving node (e.g., base station 110) can be associated with or cancomprise a single cell (e.g., cell 112) or a cluster of cells (e.g.,cell 112, cell 114, and/or cell 116, . . . ) that can be on the samebase station and can share the same location (e.g., cells of the clustercan be co-located).

In accordance with various embodiments, the estimator component 122 canemploy a machine learning (ML) engine that can utilize an ML algorithmto estimate the respective locations of the respective cells (e.g., cell112, cell 114, and/or cell 116, . . . ) of each node, based at least inpart on the results of analyzing the respective location-related data(e.g., respective location data, respective TA measurement data, and/orrespective RTD data) associated with the respective communicationdevices (e.g., communication devices 104, 106, and/or 108, . . . ), asindicated at reference numeral 204 of the cell location estimation andvalidation process 200. The ML engine can determine and generate (e.g.,as an output) estimated locations of cells (e.g., cell 112, cell 114,and/or cell 116, . . . ), as presented at reference numeral 206 of thecell location estimation and validation process 200. The estimatorcomponent 122 can apply the ML algorithm to the respective locationdata, respective TA measurement data, and/or respective RTD dataassociated with the respective communication devices (e.g.,communication devices 104, 106, and/or 108, . . . ) served by orassociated with the same node (e.g., served by or associated with thecluster of cells associated with the same node). The disclosed subjectmatter, by having the CLC 120 (e.g., estimator component 122 of CLC 120)aggregate such location-related data by node (e.g., base station 110),can enable the CLC 120 to desirably (e.g., accurately, suitably, oroptimally) estimate and validate the location(s) of a cell(s) (e.g., acell(s) associated with the node) that may have relatively few observedcommunication devices connected to or associated with the cell(s) basedat least in part on such cell(s) being co-located with another cell(s)associated with the same node.

As part of the ML engine flow employed by the estimator component 122,after grouping the data (e.g., respective location-related data) byserving node (e.g., base station 110), the estimator component 122,employing the ML engine, can determine whether there are a sufficientnumber (e.g., at least a defined threshold number) of communicationdevices (e.g., communication devices 104, 106, and/or 108, . . . )observed close to the node. If the estimator component 122 determinesthat there are a sufficient number of communication devices observedclose to (e.g., within a defined distance of) the node, the estimatorcomponent 122 can determine that a smallTA algorithm can be utilized toestimate the location of the node. If the estimator component 122determines that there is not a sufficient number of communicationdevices observed close to the node, the estimator component 122 caninstead determine that a linear regression algorithm can be utilized toestimate the location of the node.

Referring to FIG. 3 (along with FIGS. 1 and 2), FIG. 3 depicts a diagramof an example communication device distribution 300 in relation to anode (e.g., base station) associated with one or more cells, inaccordance with various aspects and embodiments of the disclosed subjectmatter. As presented in the communication device distribution 300, aplurality of communication devices (CDs), such as communication devices104, 106, 108, 302, 304, 306, 308, 310, 312, and 314, can be associatedwith (e.g., served or observed by) a node (e.g., base station 110),wherein the respective communication devices can be distributed invarious locations in relation to the node. The base station 110 can beassociated with or comprise one or more cells, such as cells 112, 114,and/or 116, that can be associated with respective communication devicesof the plurality of communication devices.

Some of the communication devices, such as communication devices 104,106, 108, 302, and 304, can be located at or within a defined thresholddistance (e.g., distance perimeter 316) of the base station 110, whereasother communication devices, such as communication devices 306, 308,310, 312, and 314, can be located further away from the base station 110outside of the defined threshold distance of the base station 110. TheCLC 120 can determine and/or set the defined threshold distance based atleast in part on (e.g., in accordance with; as indicated or specifiedby) the defined cell location management criteria, wherein the definedthreshold distance can relate to whether or not the smallTA algorithm isto be utilized by the estimator component 122 to estimate the locationof the node (and associated cells). In some embodiments, the definedthreshold distance can be 100 meters, and in other embodiments, thedefined threshold distance can be less than or greater than 100 meters,depending on the applicable cell location management criteria.

The CLC 120 can determine (e.g., calculate or derive) the respectivelocations of the respective communication devices (e.g., 104, 106, 108,302, 304, 306, 308, 310, 312, and/or 314) in relation to the node (e.g.,base station 110) based at least in part on the respective TAmeasurement data associated with the respective communication devices.The estimator component 122 can analyze the communication devicedistribution 300 to determine whether sufficient sample data points(e.g., communication device data points) exist with measured TAsindicating the associated communication devices are sufficiently close(e.g., at or within the defined threshold distance of) the location ofthe node. A smaller TA measurement can indicate that a communicationdevice is relatively close to the node, whereas a larger TA measurementcan indicate that a communication device is relatively farther away fromthe node. If the estimator component 122 determines that there is asufficient number (e.g., at least a defined threshold number) ofcommunication devices are at or within the defined threshold distance ofthe node (e.g., base station 110), the estimator component 122 candetermine that the smallTA algorithm can be utilized to estimate thelocation of the node. The CLC 120 can determine or set the definedthreshold number based at least in part on (e.g., in accordance with; asindicated or specified by) the defined cell location managementcriteria.

As an example, if the defined threshold number is 5 (or less than 5) forusing the smallTA algorithm, the estimator component 122 can determinethat there are 5 communication devices (e.g., communication devices 104,106, 108, 302, and 304) located at or within the defined thresholddistance of the node and 5 other communication devices (e.g., 306, 308,310, 312, and 314) located outside the defined threshold distance of thenode (e.g., base station 110), and accordingly, can determine that thesmallTA algorithm can be utilized to estimate the location of the node.If, instead the defined threshold number is 8 for using the smallTAalgorithm, the estimator component 122 can determine that there are 5communication devices (e.g., communication devices 104, 106, 108, 302,and 304) located at or within the defined threshold distance of thenode, and accordingly, can determine that, since 5 is less than thedefined threshold number of 8, the smallTA algorithm is not to beutilized to estimate the location of the node, but rather, the linearregression algorithm is to be utilized to estimate the location of thenode.

It is to be appreciated and understood that, for reasons of brevity,clarity, and illustration, the example communication device distribution300 depicts a total of 10 communication devices, and accordingly, forreasons of brevity, clarity, and illustration, relatively small definedthreshold numbers are utilized with regard to this example fordetermining whether or not to utilize the smallTA algorithm to estimatethe node location. In accordance with various embodiments, fordetermining whether or not to utilize the smallTA algorithm to estimatea node location, the defined threshold number of communication devicesat or within the defined threshold distance of the node can be largerthan 5 communication devices or 8 communication devices, as such definedthreshold number is indicated or specified by the defined cell locationmanagement criteria. For example, the defined threshold number can be10, 15, 20, 25, . . . , 50, . . . 100, or virtually any desired numberless than or greater than 100.

If the estimator component 122 determines that there is a sufficientnumber (e.g., at least a defined threshold number of) communicationdevices located at or within the defined threshold distance of the node(e.g., base station 110), the estimator component 122 can determine thatthe smallTA algorithm can be utilized to estimate the location of thenode. In some embodiments, the estimator component 122, employing thesmallTA algorithm, can determine the respective locations of thesubgroup of communication devices at or within the defined thresholddistance, based at least in part on the respective location data (e.g.,AGPS or GPS location data) of those respective communication devices,and can estimate the location of the node (e.g., base station 110) basedat least in part on (e.g., as a function of) a median (e.g., mediandistance values) of the respective locations (e.g., respective AGPS orGPS locations) of those respective communication devices. It is to beappreciated and understood that, while, in some embodiments, the mediandistance can be utilized to estimate the location of the node, in otherembodiments, the estimator component 122 can utilize the average or meandistance value, or another desired type of mathematically deriveddistance value, to estimate the location of the node based at least inpart on the respective locations (e.g., respective AGPS or GPSlocations) of those respective communication devices.

If the estimator component 122 determines that there is not a sufficientnumber (e.g., there is less than the defined threshold number of)communication devices located at or within the defined thresholddistance of the node (e.g., base station 110), the estimator component122 can determine that the smallTA algorithm is not to be utilized, andinstead, the linear regression algorithm is to be utilized, to estimatethe location of the node. Accordingly, the estimator component 122 canapply the linear regression algorithm to estimate the location of thenode (e.g., base station 110) based at least in part on the respectiveTA measurement data and/or the respective location data (e.g., AGPS orGPS location data) associated with the respective communication devicesand the linear regression algorithm. When the estimator component 122(e.g., ML engine of the estimator component 122) is employing the linearregression algorithm, the node location (S_(x), S_(y)) can be thecoefficients of linear equations formed by pairs of communication devicelocations and their respective distances to the node. Since there oftencan exist many pairs of communication devices to be considered withrespect to a node, the estimator component 122 can apply robust linearregression to remove outlier location-related data and mitigate (e.g.,neutralize, reduce, or minimize) noise or errors that may exist in theTA measurement data and/or location data (e.g., AGPS or GPS locationdata).

Turning briefly to FIG. 4 (along with FIGS. 1, 2, and 3), FIG. 4illustrates a diagram of an example communication device pairdistribution 400 in relation to a node (e.g., base station) associatedwith one or more cells, in accordance with various aspects andembodiments of the disclosed subject matter. The example communicationdevice pair distribution 400 depicts one pair of communication devices,communication devices 402 and 404, of a plurality of communicationdevices that can be associated with a node (e.g., base station 110) thatcan comprise or be associated with one or more cells (e.g., cells 112,114, and/or 116).

For the pair of communication devices 402 and 404, the estimatorcomponent 122 can determine that communication device 402 is located at(x₁, y₁) based at least in part on the result of analyzing thelocation-related data (e.g., TA measurement data and/or AGPS or GPSdata) associated with the communication device 402, and can determinethat communication device 404 is located at (x₂, y₂) based at least inpart on the result of analyzing the location-related data associatedwith the communication device 404. Each pair of communication devices(e.g., communication devices 402 and 404) can form a linear equation ofthe node location, wherein (S_(x), S_(y)) can be the node location,(x_(i), y_(i)), with i=1, 2, can be any pair of device locations of anypair of communication devices of the plurality of communication devices,and d1 (e.g., d1 406) and d2 (e.g., d2 408) can be the respectivedistances derived from the TA measurement data associated with therespective communication devices of the device pair to the node (e.g.,serving node).

The estimator component 122 can perform similar linear regressionanalysis calculations for all or a desired portion of the various pairsof communication devices associated with the node (e.g., base station110) to facilitate estimating the location of the node. The estimatorcomponent 122 can estimate the location of the node based at least inpart on the respective locations of the respective intersection points(e.g., intersection point 410) of the respective pairs of communicationdevices associated with the node, in accordance with the linearregression algorithm. With respect to the pair of communication devicesunder consideration, the estimator component 122 can determine orestimate the location of the node (e.g., base station 110) to be at theintersection point 410 of two circles 412 and 414 that can berespectively centered at the respective locations of the communicationdevices 402 and 404 of the device pair, with respective radii that canbe equal to the respective distances d1 406 and d2 408 of the respectivecommunication devices 402 and 404 to the node. These parameters cansatisfy the following example equations:(x ₁-s _(x))²+(y ₁-s _(y))² =d ₁ ²;  Eq. (1)(x ₂-s _(x))²+(y ₂-s _(y))² =d ₂ ²;  Eq. (2)Eq.(1)-Eq.(2):(x ₂-x ₁)s _(x)+(y ₂-y ₁)s _(y)=½[(d ₂ ² −d ₁ ²)+(x ₂ ² −x ₁ ²)+(y ₂ ²−y ₁ ²)],  Eq. (3)and where β_(x) s _(x)+β_(y) s _(y) =C,  Eq. (4)wherein there can be one sample of (β_(x), β_(y), C) for each pair ofcommunication device locations (e.g., (x_(i), y_(i)), with i=1, 2).

With further regard to FIGS. 1 and 2, the CLC 120 can estimate therespective locations of the one or more cells (e.g., cell 112, cell 114,and/or cell 116, . . . ) of the node (e.g., base station 110) to be theestimated location of the node. For instance, the CLC 120 can determinethat the estimated location of each cell (e.g., cell 112, cell 114, cell116) associated with a particular node (e.g., base station 110) can bethe estimated location of the node (e.g., as determined by the smallTAalgorithm or the linear regression algorithm).

For each cell (e.g., cell 112, cell 114, or cell 116, . . . ) of eachnode (e.g., base station 110), with the location of the node estimatedby the estimator component 122 (e.g., employing the ML engine), the CLC120 can determine that the estimated location of the cell is theestimated location of the node. For each cell (e.g., cell 112, cell 114,or cell 116, . . . ), the CLC 120, employing the validator component124, can validate or verify the estimated location of the cell and/or arecorded location(s) of the cell to facilitate determining whether theestimated node location and/or recorded node location(s) is accurate(e.g., sufficiently accurate) or not, or is uncertain, in accordancewith the defined cell location management criteria, as indicated atreference numeral 208 (cell location validation) of the cell locationestimation and validation process 200. To perform the cell locationvalidation 208, the validator component 124 can utilize one or morevarious types of measurements to validate (e.g., determine the accuracyof) a location of a cell. The validator component 124 can employ a setof rules (e.g., set of validation rules) to facilitate validating alocation of a cell, wherein one or more rules of the set of rules can beapplied to one or more measurement results to facilitate determining anaccuracy level of a potential cell location (e.g., an estimated celllocation, or a recorded cell location obtained from a data source asstored in the cell location pool of the cell location pool component118), in accordance with the defined cell location management criteria.

In some embodiments, to facilitate validating the location of a cell(e.g., cell 112, cell 114, or cell 116), the validator component 124 candetermine (e.g., calculate) a distance difference (DD) as a function ofa first distance, D1, between a potential location (e.g., an estimatedlocation, or a recorded location from a data source) of the cell and alocation of a communication device (e.g., communication device 104,communication device 106, or communication device 108) and a seconddistance, D2, which can be a measurement distance (e.g., converted fromTA measurement data) between a true location of the cell and thecommunication device, as more fully described herein. It is to beappreciated and understood that the first distance, D1, and the seconddistance, D2, utilized to determine a DD are different from thedistances d1 and d2 (e.g., distances d1 406 and d2 408 of FIG. 4)utilized during the linear regression analysis. The validator component124 can determine the DDs with regard to all or a desired portion of thecommunication devices associated with (e.g., served by, observed by, orotherwise associated with) the cell. If the cell location information(e.g., from a recorded cell location of a data source, or from anestimated cell location), is at the true cell location or is very closeto the true cell location, the DD can be expected to be close to 0.Thus, if there are a lot of small DD values associated with a cell, thiscan be a good indicator that the cell location information (e.g.,recorded cell location, or estimated cell location) is acceptablyreliable (e.g., acceptably accurate).

Referring briefly to FIG. 5 (along with FIGS. 1 and 2), FIG. 5 depicts adiagram of example distance differences (DDs) 500 of respective firstdistances between a potential location (e.g., estimated location orrecorded location) of a cell and respective locations of communicationdevices and respective second distances that can be respectivemeasurement distances (e.g., converted from TA measurement data) betweena true location of the cell and the respective communication devices, inaccordance with various aspects and embodiments of the disclosed subjectmatter. In some embodiments, to facilitate cell location validation 208of a location of a cell (e.g., cell 112, cell 114, or cell 116, . . . ofFIG. 1), for each communication device (e.g., communication device 104,communication device 106, and/or communication device 108 of FIG. 1)associated with the cell, the validator component 124 can determine a DDbetween the reported distance from the communication device to the cellbased on TA measurement data and the determined (e.g., calculated)distance from a location (x1, y1) of a communication device 502 derivedfrom location data (e.g., AGPS or GPS) to the potential location 504(Sx, Sy) of the cell (POT. CELL) in the record (e.g., a recorded celllocation of a data source as stored in the cell location pool of thecell location pool component 118, or the estimated cell locationdetermined by the estimator component 122).

If, for instance, all data sources for cell location (e.g., estimatedcell location and a recorded cell location) of a cell are accurate, thereported distance from a communication device to the cell based on TAmeasurement data should match the determined (e.g., calculated) distancefrom a location (x1, y1) of the communication device 502 derived fromlocation data (e.g., AGPS or GPS) to the potential location 504 (Sx, Sy)of the cell in the record (e.g., a recorded cell location of a datasource, or the estimated cell location). The disclosed subject mattercan define an error, ERR, as being the distance between a potential celllocation 504 (Sx, Sy) (e.g., recorded cell location or estimated celllocation) of a cell and the true location 506 (TRUE CELL) of the cell.

The validator component 124 can determine (e.g., calculate) a DD foreach communication device record (e.g., TA measurement data) associatedwith each communication device (e.g., communication device 502,communication device 508, communication device 510, communication device512, and/or communication device 514) that is associated with the cell506 (e.g., cell at true location), for example, as follows. Thevalidator component 124 can determine (e.g., calculate) a firstdistance, D1, between the communication device-reported location (x1,y1) (e.g., AGPS or GPS location) of the communication device 502 to apotential cell location 504 (Sx, Sy) (e.g., estimated cell location or arecorded cell location obtained from a data source of the cell locationpool). In some embodiments, the validator component 124 can determinethe first distance, D1, with regard to communication device 502 usingEq. (5) as follows:D1=√{square root over ((x ₁ −s _(x))²+(y ₁ −s _(y))².)}  Eq. (5)

The validator component 124 also can determine respective firstdistances, D1 s, for the other communication devices (e.g., 508, 510,512, and/or 514), as a function of the respective communicationdevice-reported locations of the other communication devices and thepotential cell location 504, in a manner similar to that describedherein with regard to communication device 502.

The validator component 124 also can determine (e.g., calculate) asecond distance, D2, with regard to the communication device 502 as afunction of the TA measurement data associated with the communicationdevice and the cell (at the true cell location) and the multipath effect(e.g., D2=TA−multipath effect). In some embodiments, the validatorcomponent 124 can determine the second distance, D2, with regard tocommunication device 502 using Eq. (6) as follows:D2=TA*78 m−multipath effect,  Eq. (6)wherein TA can be the TA measurement data as converted to the unit ofmeters, and 78 m can be a function value, wherein the multipath effectcan relate to signals (e.g., radio signals) communicated between acommunication device and a cell being propagated in different ways andacross different distances due in part to the scattering and reflectingof signals, and wherein it can be desirable to take the multipath effectinto account when determining the second distance, D2, because themultipath effect can affect or impact the determination of the seconddistance, D2, using the TA measurement data. The validator component 124also can determine respective second distances, D2s, for the othercommunication devices (e.g., 508, 510, 512, and/or 514), as a functionof the respective TA measurement data associated with the respectiveother communication devices and the cell 506 at the true cell location,the respective multipath effects associated with the respective othercommunication devices, and the function value, in a manner similar tothat described herein with regard to communication device 502.

The validator component 124 can determine the DDs associated with eachpotential cell location 504 (Sx, Sy) (e.g., estimated cell location or arecorded cell location) of the cell as a function of the first distance,D1, and the second distance, D2, with respect to each communicationdevice (e.g., 502, 508, 510, 512, and/or 514). In some embodiments, thevalidator component 124 can determine the DD for each potential celllocation 504 (Sx, Sy) with respect to each communication device usingEq. (7):DD=abs(D1−D2)≤ERR,  Eq. (7)wherein abs can be an absolute value function that can take the absolutevalue of D1-D2, and wherein abs(D1-D2) can be less than or equal to ERR.If the potential cell location (e.g., position) 504 (Sx, Sy) isaccurate, ERR=0, and accordingly, D1=D2=TA−multipath effect.

The validator component 124 can determine the DD for each communicationdevice record (e.g., each record from each data source in the celllocation pool, and the record for the estimated cell location)associated with each communication device (e.g., 502, 508, 510, 512,and/or 514) under consideration.

In certain embodiments, to facilitate validating the location (e.g.,potential location) of a cell (e.g., cell 506), the validator component124 can utilize DD values associated with the communication devices(e.g., 502, 508, 510, 512, and/or 514) and the cell to determinerespective upper bound values (UB) of ERR of communication devices(e.g., 502, 508, 510, 512, and/or 514) associated with (e.g., served by,observed by, or otherwise associated with) the cell, wherein the ERR canbe the distance between a potential cell location 504 (Sx, Sy) (e.g.,recorded cell location or estimated cell location) of a cell and thetrue location 506 of the cell. For each communication device (e.g., 502,508, 510, 512, and/or 514) associated with a cell, a UB of ERR, withrespect to a communication device and the cell, can be determined as afunction of the first distance, D1, TA measurement data (as converted tometers), and a defined factor (e.g., the function value). For example, aUB of ERR, with respect to a communication device and the cell, can bedetermined (e.g., calculated) using the following Eq. (8):UB=min(D1+TA*78 m)  Eq. (8)of communication devices associated with (e.g., served by) the cell,wherein min can be the minimum function. From triangle inequality, itcan be observed that ERR≤D1+D2≤D1+TA*78 m for any communication deviceassociated with (e.g., served by) the cell, and ERR≤min(D1+TA*78 m).Also, D1+D1≥ERR for each communication device record observed.

With further regard to FIGS. 1 and 2, and with further regard toperforming cell location validation 208, the validator component 124 candetermine (e.g., calculate) the respective UBs of ERR associated withthe respective communication devices (e.g., communication device 104,communication device 106, and/or communication device 108, . . . )associated with the cell (e.g., cell 112, cell 114, or cell 116) basedat least in part on (e.g., as a function of) respective first distances,respective D1 s, and respective TA measurement data (and a defineddistance factor) associated with the respective communication devices,as more fully described herein. The validator component 124 also candetermine whether the respective UBs of ERR associated with a firstportion of the respective communication devices in a first definedpercentile (e.g., a bottom or lower end percentile, such as 1percentile, or another desired percentile value) satisfy the firstdefined threshold distance associated with the first rule, in accordancewith the defined cell location management criteria.

If the validator component 124 determines that the respective UBs of ERRassociated with the first portion of the respective communicationdevices (e.g., communication device 104, communication device 106,and/or communication device 108, . . . , if in the first portion) in thefirst defined percentile satisfy the first defined threshold distance(e.g., 150 meters, or other desired distance greater than or less than150 meters) associated with a first rule of the set of rules, inaccordance with the defined cell location management criteria, thevalidator component 124 can determine that the potential (e.g., recordedor estimated) location of the cell is good and can flag the potentiallocation of the cell as being good (or with a similar flag, such as anaccurate flag).

If, instead, the validator component 124 determines that one or more ofthe respective UBs of ERR associated with the first portion of therespective communication devices (e.g., communication device 104,communication device 106, and/or communication device 108, . . . , if inthe first portion) in the first defined percentile do not satisfy thefirst defined threshold distance associated with the first rule, thevalidator component 124 can determine that the potential location of thecell (e.g., cell 112, cell 114, or cell 116) is not to be labeled orflagged as good and is not to be tagged as accurate. The validatorcomponent 124 also can determine that further analysis is to beperformed to facilitate determining whether the potential cell locationis to be flagged as fine, is to be flagged as bad, or is to be flaggedas uncertain, based at least in part on one or more of the rules (e.g.,first rule, second rule, and/or third rule) of the set of rules.

If the validator component 124 determines that the first rule is notsatisfied with regard to the UB of ERR, the validator component 124 cananalyze (e.g., compare) the respective DDs associated with a secondportion of the respective communication devices (e.g., communicationdevice 104, communication device 106, and/or communication device 108, .. . , if in the second portion) in a second defined percentile (e.g.,another lower end percentile, such as the 25^(th) percentile, which canbe the bottom 25% of the respective DDs associated with the respectivecommunication devices, or another desired lower end percentile value)satisfy the second defined threshold distance associated with the secondrule. Based at least in part on the analysis results, the validatorcomponent 124 can determine whether the respective DDs associated withthe second portion of the respective communication devices in the seconddefined percentile satisfy (e.g., are less than or equal to) the seconddefined threshold distance associated with the second rule. Inaccordance with various embodiments, the second defined thresholddistance can be the same as the first defined threshold distance (e.g.,150 meters (or other desired distance)) or can be different from thefirst defined threshold distance, as indicated or specified by thedefined cell location management criteria.

In some embodiments, if the validator component 124 determines that therespective DDs associated with the second portion of the respectivecommunication devices in the second defined percentile satisfy thesecond defined threshold distance associated with the second rule, thevalidator component 124 can perform further analysis to facilitatevalidating the location of the cell (e.g., cell 112, cell 114, or cell116, . . . ). For instance, the validator component 124 can perform across validation of the estimated location of the cell and (e.g.,vis-à-vis, or in relation to) one or more recorded locations of the cellobtained from one or more data sources (e.g., and stored in the celllocation pool component 118). Whether the validator component 124 flagsa potential (e.g., estimated or recorded) cell location of the cell asgood or fine can be determined based at least in part on such crossvalidation.

For example, for the cell (e.g., cell 112, cell 114, or cell 116, . . .), the validator component 124 can determine a difference between arecorded cell location (R) obtained from a data source (e.g., for eachrecorded cell location for each data source) and the estimated celllocation (E) of the cell (wherein such difference also can be referredto as D_RE). Since the estimator component 122 (e.g., employing the MLengine) can estimate the cell location based on network measurements(e.g., TA measurement data, AGPS or GPS data), the estimated celllocation can be independent of the cell location(s) recorded in anotherdata source(s) (e.g., as stored in the cell location pool in the celllocation pool component 118). If D_RE is sufficiently small (e.g., lessthan a defined threshold distance), it can be relatively good evidencethat both the estimated location of the cell and the recorded locationof the cell from the data source are both reliable (e.g., sufficientlyaccurate in establishing the location of the cell).

The validator component 124 can determine the distance between arecorded location of the cell (e.g., cell 112, cell 114, or cell 116, .. . ) and the estimated location of the cell, wherein the recordedlocation can be obtained from the cell location pool component 118, andwherein can perform such a D_RE determination with regard to one or morerecorded locations from one or more data sources (e.g., ATOLL and/orCSSNG, . . . ), as stored in the cell location pool component 118. Withregard to each of the one or more D_REs and the associated one or morerecorded locations, the validator component 124 can compare the distancebetween the recorded location and the estimated location of the cell tothe first defined threshold distance (e.g., 150 meters, or other desireddistance greater than or less than 150 meters), which can be indicatedor specified by the first rule of the set of rules, in accordance withthe defined cell location management criteria. Based at least in part onthe results of the comparison, the validator component 124 can determinewhether the distance between the recorded location and the estimatedlocation of the cell (the D_RE) satisfies the first defined thresholddistance associated with the first rule.

If the validator component 124 determines that the distance between therecorded location and the estimated location of the cell (the D_RE)satisfies the first defined threshold distance, based on suchdetermination and the determination that the first rule is satisfiedwith regard to the UB of ERR, the validator component 124 can determinethat the recorded location and the estimated location are good and canflag the recorded location and the estimated location as good. If therecorded location and the estimated location of the cell are determinedto be good and flagged as good, the cell location validation 208 can becomplete with regard to that particular cell.

If the validator component 124 determines that the distance between therecorded location and the estimated location of the cell (the D_RE) doesnot satisfy (e.g., is not less than or equal to) the first definedthreshold distance, in accordance with the first rule, the validatorcomponent 124 can determine that the potential (e.g., recorded orestimated) location of the cell (e.g., cell 112, cell 114, or cell 116,. . . ) is fine (e.g., not sufficiently accurate, but not bad oruncertain either) and can flag that particular cell location as fine (oracceptable, or with another descriptive term that can indicate fine oracceptable).

With further regard to the DDs, if, instead, the validator component 124determines that one or more of the respective DDs associated with thesecond portion of the respective communication devices (e.g.,communication device 104, communication device 106, and/or communicationdevice 108, . . . , if in the second portion) in the second definedpercentile do not satisfy the second defined threshold distanceassociated with the second rule, the validator component 124 candetermine that the potential location of the cell is not to be labeledor flagged as fine. The validator component 124 also can determine thatfurther analysis is to be performed to facilitate determining whetherthe potential cell location is to be flagged as bad (or with anothersimilar term that can indicate bad or unacceptable) or is to be flaggedas uncertain, based at least in part on another rule (e.g., third rule)of the set of rules.

In response to determining that the potential location (e.g., recordedlocation, or estimated location) of the cell (e.g., cell 112, cell 114,or cell 116) is not to be labeled or flagged as fine (or good), thevalidator component 124 can determine the number of communicationdevices in the second portion of communication devices in the seconddefined percentile, based at least in part on the analysis results.Further, based at least in part on the analysis results, the validatorcomponent 124 can determine whether the number of communication devicesin the second portion of communication devices in the second definedpercentile satisfies (e.g., meets or exceeds) the defined thresholdnumber (e.g., 30 or other desired number less than or greater than 30)of communication devices associated with the third rule. The definedthreshold number can be indicated or specified by the defined celllocation management criteria.

If the validator component 124 determines that the respective DDsassociated with the second portion of the respective communicationdevices in the second defined percentile satisfy the second definedthreshold distance associated with the second rule, the validatorcomponent 124 can determine that the potential (e.g., recorded orestimated) location of the cell (e.g., cell 112, cell 114, or cell 116)is bad or unacceptable, and can flag the potential location of the cellas being bad or unacceptable. If, instead, the validator component 124determines that the number of communication devices in the secondportion of communication devices in the second defined percentile doesnot satisfy (e.g., is not greater than or equal to) the definedthreshold number associated with the third rule, the validator component124 can determine that the potential location of the cell is to beflagged as uncertain.

The validator component 124, by determining the UBs of ERR and the DDsat cell level, as opposed to node level, can identify instances where acell, while associated with a node, is not necessarily located at thenode or co-located with other cells associated with (e.g., co-locatedat) the node. Thus, even though the CLC 120 estimates the location ofthe node and, accordingly, estimates the locations of the cellsassociated with the node (e.g., at node level), the validator component124 can determine whether one or more of the cells are not actuallyco-located with the node and other cells, to facilitate desirably (e.g.,accurately, or at least more accurately) determine the respectivelocations of respective cells associated with the node, and desirably(e.g., accurately) determine the validation status(es) of one or morepotential (e.g., estimated or recorded) locations of each cell.

With the cell location validation 208 performed, with regard to eachcell (e.g., cell 112, cell 114, or cell 116, . . . ) of each node, basedat least in part on the results of the cell location validation 208, thevalidator component 124 can perform cell location tagging 210 to tag theone or more locations (e.g., estimated cell location, and/or one or morerecorded cell locations) of the cell with an appropriate tag (e.g., tagthat can correspond to the cell location validation result andassociated flag), in accordance with the cell location managementcriteria. For instance, if the validator component 124 determines that aparticular cell location (e.g., an estimated cell location, or arecorded cell location from a particular data source) is sufficientlyaccurate and flags that particular cell location as good (or accurate,or with another descriptive term that indicates good or accurate), thevalidator component 124 can tag that particular cell location asaccurate (or with another suitably equivalent term), wherein, forexample, the validator component 124 can assign or associate (e.g.,link) a tag of accurate (or another suitably equivalent term) to or withthe particular cell location. If the validator component 124 determinesthat a particular cell location is fine (e.g., not sufficientlyaccurate, but not bad or uncertain either) and flags that particularcell location as acceptable (or fine, or with another descriptive termthat indicates fine or acceptable), the validator component 124 can tagthat particular cell location as acceptable (or with another suitablyequivalent term), wherein, for example, the validator component 124 canassign or associate (e.g., link) a tag of acceptable (or anothersuitably equivalent term) to or with that particular cell location. Insome embodiments, with regard to a particular cell (e.g., cell 112), thevalidator component 124 can determine and select the best (e.g., mostaccurate) cell location and associated best data source of all of thecell locations and associated data sources (e.g., estimated celllocation from the estimator component 122, and all of the one or morerecorded cell locations from one or more data sources) for theparticular cell, and can flag that best cell location and associatedbest data source as fine and tag them as acceptable (or with anotherdescriptive term that indicates fine or acceptable).

If the validator component 124 determines that a particular celllocation is bad (e.g., not sufficiently accurate or acceptable, and notuncertain) and flags that particular cell location as bad (orinaccurate, or with another descriptive term that indicates bad), thevalidator component 124 can tag that particular cell location asinaccurate (or with another suitably equivalent term), wherein, forexample, the validator component 124 can assign or associate (e.g.,link) a tag of inaccurate (or another suitably equivalent term) to orwith that particular cell location. In some embodiments, if with regardto a particular cell (e.g., cell 112), the validator component 124determines that all of the data sources (e.g., estimated cell locationfrom the estimator component 122, and all of the one or more recordedcell locations from one or more data sources) are bad, the validatorcomponent 124 can tag all of those cell locations and associated datasources as being inaccurate (or with another suitably equivalent term).If the validator component 124 determines that a particular celllocation is uncertain (e.g., there is not enough certainty to indicatewhether the cell location is accurate, acceptable, or bad) and flagsthat particular cell location as uncertain (or with another descriptiveterm that indicates uncertain), the validator component 124 can tag thatparticular cell location as uncertain (or with another suitablyequivalent term), wherein, for example, the validator component 124 canassign or associate (e.g., link) a tag of uncertain (or another suitablyequivalent term) to or with that particular cell location.

Based at least in part on the tag assigned to or associated with aparticular cell location of a particular cell (e.g., cell 112, cell 114,or cell 116), the CLC 120 (e.g., the validator component 124 or anothercomponent of the CLC 120) can initiate a cell location investigation212, a cell location lock 214, or a cell monitoring request 216, withrespect to the particular cell location of the particular cell. Forinstance, for a particular cell location (e.g., cell 112), if thevalidator component 124 associates a tag of inaccurate with theparticular cell location, the CLC 120 can initiate a cell locationinvestigation 212 to have an investigation (e.g., a manual investigationon map, or a physical visit to the particular cell) performed to try todetermine the true location of the cell. For example, as part of a celllocation investigation 212 to investigate a location of a particularcell, a manual or an automated investigation on map can be performed(e.g., manually or by the CLC 120) by plotting a UE traffic heatmap on amap and plotting the estimated location (e.g., ML estimated location)and recorded cell location(s) of a data source(s) on the map, withrespect to the particular cell. If the estimated location is determinedto be significantly closer to the UE population (e.g., from the UEtraffic heatmap) than the recorded location(s) of the data source(s), itcan indicate or suggest that the data source(s) is an issue (e.g.,indicate that the data source(s) is in error).

Referring briefly to FIG. 6 (along with FIGS. 1 and 2), FIG. 6 presentsa diagram of an example map plot 600 that can include a UE trafficheatmap, an estimated location of a cell, and a recorded location of thecell that are plotted on the map, in accordance with various aspects andembodiments of the disclosed subject matter. The map plot 600 can be ofan example geographical area (e.g., geographical area near a city). TheCLC 120 (or another component of or associated the system 100) can plota UE traffic heatmap on the map plot 600, wherein the UE traffic heatmapcan present the population or distribution of UEs 602 (e.g.,communication devices) that are determined to have been associated withthe cell. The CLC 120 (or another component of or associated the system100) also can plot the estimated cell location 604 of the cell on themap plot 600. Further, the CLC 120 (or another component of orassociated the system 100) can plot a recorded cell location 606 (e.g.,E911_DataSource) of the cell on the map plot 600, wherein the recordedcell location 606 can be a cell location that has been obtained from adata source and has been recorded in (e.g., stored in) the cell locationpool of the cell location pool component 118. The CLC 120 (or anothercomponent of or associated the system 100) or a person can analyze themap plot 600 with the various plots (e.g., 602, 604, 606) thereon. Ifthe CLC 120 (or another component of or associated the system 100) or aperson determines that the estimated cell location 604 is significantlycloser to the population or distribution of UEs 602 than the recordedcell location 606 on the map plot 600 (as depicted in the map plot 600),the CLC 120 (or another component of or associated the system 100) orthe person can determine that the plots (e.g., 602, 604, 606) on the mapplot 600 indicate that there may be an issue with the recorded celllocation 606 of the data source. Alternatively, if the CLC 120 (oranother component of or associated the system 100) or a persondetermines that the estimated cell location 604 is significantly furtheraway from the population or distribution of UEs 602 than the recordedcell location 606 on the map plot 600, the CLC 120 (or another componentof or associated the system 100) or the person can determine that theplots (e.g., 602, 604, 606) on the map plot 600 indicate that there maybe an issue with the estimated cell location 604. In this alternativeinstance, the estimated cell location 604 being located significantlyfurther away from the population or distribution of UEs 602 than therecorded cell location 606 can indicate, for example, that the antennaassociated with the cell or node may be located at a different locationthan the base station tower associated with the cell or node.

An additional or an alternative approach to the manual investigation onmap can be to perform a physical visit (e.g., by a person or a device,such as a drone device) to the estimated cell location (e.g., estimatedML location) and the recorded cell location(s) of the data source(s)obtained from the cell location pool component 118 to determine which,if any, of the estimated cell location or the recorded cell location(s)is the accurate (e.g., true) location of the cell. A physical visit tothe estimated cell location and recorded cell location(s) of a cell canbe more expensive (e.g., more expensive financially, more timeconsuming, and/or more resource intensive, . . . ) than performing amanual investigation on map with regard to the cell. However, it may bedesirable to perform such physical visits with regard to some cells(e.g., a relatively small amount of cells). Major cells typically can bevalidated with the assistance of the map and UE traffic distribution.

For a particular cell, based at least in part on the results of themanual investigation on map or the physical visit, the CLC 120 (oranother component of or associated the system 100) or the person canupdate the cell location pool of the cell location pool component 118 tostore the cell location of the cell as determined from the results ofthe manual investigation on map or the physical visit, as indicated atreference numeral 218 of the cell location estimation and validationprocess 200.

With further regard to FIGS. 1 and 2, in response to determining that aparticular location (e.g., estimated cell location or a particularrecorded cell location of a particular data source) of a cell isaccurate (e.g., sufficiently accurate) and is tagged as accurate, theCLC 120 can initiate a cell location lock 214 and can lock or facilitatelocking the particular cell location and particular cell with anaccurate tag (e.g., or tag them with an equivalent term, such as good)to prevent the particular cell location from being undesirably (e.g.,inadvertently or incorrectly, or unexpectedly) changed in the celllocation pool of the cell location pool component 118. The CLC 120 canupdate the cell location pool of the cell location pool component 118 toinclude the particular location of the cell, which was determined to beaccurate, the cell identifier that can identify the particular cell, thelock tag, and/or other desired information regarding the cell in thecell location pool of the cell location pool component 118, as indicatedat reference numeral 220 of the cell location estimation and validationprocess 200.

In some embodiments, in response to determining that a certain location(e.g., estimated cell location or a certain recorded cell location of acertain data source) of a certain cell is uncertain and is tagged asuncertain, the CLC 120 can initiate the cell monitoring request 216 tohave the certain cell monitored for a desired (e.g., longer) period oftime to collect data (e.g., location-related data) relating to thecertain cell (e.g., as indicated at reference numeral 202) and havefurther analysis performed on such data (e.g., by the CLC 120). Forexample, based at least in part on the cell monitoring request 216,instead of collecting data for a relatively short time period (e.g., oneday), as indicated at reference numeral 202, the CLC 120 can collectdata (e.g., location-related data) associated with the certain cell fora relatively longer time period (e.g., one week, one month, or otherdesired period of time that is longer than the short time period) tofacilitate obtaining a desired amount of sample data points (e.g., datapoints of location-related data) for the certain cell. Collecting suchdata for a relatively longer period of time can be useful, for example,with regard to cells (e.g., cells in a rural area) that may beassociated with a sparse number of communication devices or sparse useby communication devices.

The disclosed subject matter, by employing the CLC 120 to estimate thelocations of nodes and associated cells, based at least in part on TAmeasurement data and location data associated with communicationdevices, and using the location estimation algorithms (e.g., smallTAalgorithm, linear regression algorithm, and/or other machine learningalgorithms), and validating potential cell locations of cells (e.g.,estimated cell locations and recorded cell locations), based at least inpart on TA measurement data and location data associated withcommunication devices, and using the validation algorithms, such asdescribed herein, can desirably (e.g., accurately, or at leastsubstantially accurately) determine locations of cells. For example, theCLC 120 can desirably and accurately determine or estimate locations ofcells with median errors of approximately 50 meters, which can besignificantly better than traditional techniques for determining celllocations, as some traditional techniques for determining cell locationscan have undesirable errors on the order of 300 meters or more. Thedisclosed subject matter, by utilizing the CLC 120 and techniquesdescribed herein, can involve relatively low resource usage, and thedisclosed subject matter can utilize existing network measurements(e.g., TA measurement data, and AGPS, GPS, and/or IoT geolocation data)to estimate and validate cell locations, and thus, can incur noadditional or incremental burden on network data collection. Also, thelocation estimation algorithms and validation algorithms disclosedherein can be relatively easy to implement with relatively low resourceusage (e.g., computing resource usage and/or time resource usage).

Further, since the disclosed subject matter can be collected by the CLC120 consistently from the live communication network 102, the CLC 120can be able to monitor any change (e.g., adding, moving, or removing) ofcells in the communication network 102, and can maintain up-to-date, orat least substantially up-to-date, cell location information of thecells. Furthermore, the disclosed subject matter, by employing the CLC120 and the associated location estimation algorithms and validationalgorithms described herein, can have desirable scalability, and thetechniques and algorithms described herein can be scaled up for use withregard to virtually any communication network of virtually any desiredsize, and with regard to virtually any number of communication networks.

The disclosed subject matter also can have various commercial benefits.For instance, the disclosed subject matter, by employing the CLC 120 andassociated techniques and algorithms, as described herein, can providecost savings (e.g., financial cost savings, time cost saving, and/orother cost savings), as the disclosed subject matter can desirably(e.g., effectively, efficiently, and accurately) estimate cell locations(e.g., by providing ML estimated cell locations) and validate variouscell locations and associated cell location data sources. This cansignificantly reduce the amount of manual labor associated with havingpeople (e.g., technicians, contractors, or other persons) travel to cellsites or potential cell sites. Further, the disclosed subject matter, byemploying the CLC 120 and associated techniques and algorithms, asdescribed herein, can enable desirable (e.g., accurate, efficient, andeffective) communication network planning and design, and E911 dispatchoperations. For instance, the disclosed subject matter can benefit RANplanning and design by providing desirably accurate cell locationinformation, which can reduce the amount of time and the amount offinancial cost involved in planning and designing RANs. The disclosedsubject matter also can aid and provide benefit in the deployment of the5G network. Also, with regard to E911 operations, the disclosed subjectmatter, by providing desirably accurate cell location information, canenable an E911 dispatch team to desirably and accurately identify PSAPcall routing/caller location, during an E911 call, and provide desiredemergency assistance in a more timely (e.g., quicker) manner.

FIG. 7 illustrates a block diagram of the example CLC 700, in accordancewith various aspects and embodiments of the disclosed subject matter.The CLC 700 can include a communicator component 702, an operationsmanager component 704, a data collector component 706, an estimatorcomponent 708, which can include a machine learning component 710. TheCLC 700 also can include a validator component 712 that can include adistance determination component 714, a rules component 716, and a tagcomponent 718. The CLC 700 further can include a lock component 720, acell investigation component 722, a cell monitoring request component724, a processor component 726, and a data store 728.

The communicator component 702 can communicate (e.g., transmit andreceive) information, including information relating to cell locationdeterminations, such as information relating to estimating locations ofcells and validating potential cell locations (e.g., an estimated celllocation, or a recorded cell location) of cells. For instance, thecommunicator component 702 can receive data relating to the location ofcommunication devices (e.g., TA measurement data, AGPS or GPS locationdata, or IoT geolocation data, . . . ) associated with cells. Thecommunicator component 702 also can transmit information to othercomponents or devices (e.g., cell pool location component, networkdevices of the communication network, . . . ) associated with the CLC700. For instance, the communicator component 702 can transmitinformation relating to a cell location of a cell (e.g., cell locationinformation relating to an estimated cell location and/or a recordedcell location, and/or tag information relating to cell location accuracyand/or locking of cell location information, . . . ), informationrelating to initiating cell location investigations, and/or informationrelating to cell monitoring requests, etc.

The operations manager component 704 can control (e.g., manage)operations associated with the CLC 700. For example, the operationsmanager component 704 can facilitate generating instructions to havecomponents of the CLC 700 perform operations, and can communicaterespective instructions to respective components (e.g., communicatorcomponent 702, data collector component 706, estimator component 708, .. . , cell investigation component 722, cell monitoring requestcomponent 724, processor component 726, and/or data store 728) of theCLC 700 to facilitate performance of operations by the respectivecomponents of the CLC 700 based at least in part on the instructions, inaccordance with the defined cell location management criteria and celllocation management algorithms (e.g., machine learning algorithms,validation algorithms, etc., as disclosed, defined, recited, orindicated herein by the methods, systems, and techniques describedherein). The operations manager component 704 also can facilitatecontrolling data flow between the respective components of the CLC 700and controlling data flow between the CLC 700 and another component(s)or device(s) (e.g., cell pool location component, network devices of thecommunication network, data sources, or applications, . . . ) associatedwith (e.g., connected to) the CLC 700.

The data collector component 706 can collect and aggregate data,including, for example, call trace records (e.g., TA measurement datafrom call trace records) and location data (e.g., AGPS or GPS locationdata, or IoT geolocation data) associated with communication devices,and can store such data in the data store 728. The data collectorcomponent 706 also can receive or obtain data, such as data relating torecorded cell locations, from the cell location pool component.

The estimator component 708 can estimate cell locations of cells basedat least in part on the results of analyzing location-related dataassociated with communication devices, in accordance with the definedcell location management criteria, as more fully described herein. Inaccordance with various embodiments, the estimator component 708 canemploy a smallTA algorithm, a linear regression algorithm, and/or othermachine learning algorithms to facilitate estimating the locations ofnodes (e.g., base stations) and cells associated with the nodes. Themachine learning component 710 can employ the machine learningtechniques and algorithms, including, for example, the linear regressionalgorithm and/or other techniques and algorithms, such as describedherein, to facilitate estimating the locations of the nodes andassociated cells, in accordance with the defined cell locationmanagement criteria.

The validator component 712 can validate and/or determine the accuracyof estimated locations of cells and recorded locations of cells (e.g.,obtained from the cell location pool component), based at least in parton the results of analyzing the estimated cell locations, recorded celllocations, and/or location-related data, in accordance with the definedcell location management criteria and associated algorithms (e.g.,validation algorithms), as more fully described herein. The validatorcomponent 712 can employ the distance determination component 714 todetermine (e.g., calculate) D_REs, DDs, and/or UBs of ERR, where suchdeterminations can be utilized to facilitate determining how accurate apotential cell location (e.g., estimated cell location or recorded celllocation) is. The validator component 712 can utilize the rulescomponent 716 to implement (e.g., apply and/or enforce) a set of rulesand respective threshold levels (e.g., defined threshold distances,defined threshold DD percentile value, and/or defined threshold UBpercentile value) associated with respective rules, such as more fullydescribed herein.

The tag component 718 can associate (e.g., link or assign) respectivetags with respective cell location information (e.g., estimated celllocation information or recorded cell location information) based atleast in part on the respective validation results of respective celllocation validations performed with regard to respective potential celllocations by the validator component 712. For instance, if a potentialcell location is validated and flagged as good, the tag component 718can associate an accurate tag with the potential cell location; if apotential cell location (e.g., the best or most accurate cell locationof those under evaluation) is validated and flagged as fine, the tagcomponent 718 can associate an acceptable tag with the potential celllocation; if a potential cell location(s) is validated and flagged asbad (e.g., if all of the potential cell locations of a cell aredetermined to be bad), the tag component 718 can associate an inaccuratetag with the potential cell location(s); or if a potential celllocation(s) is validated and flagged as uncertain (e.g., if there is notenough data to support a decision regarding cell location validation),the tag component 718 can associate an uncertain tag with the potentialcell location(s). If a potential cell location is tagged as accurate,the tag component 718 also can associate a lock tag with the potentialcell location to facilitate locking the cell location information tofacilitate preventing any undesired (e.g., unwanted, inadvertent, orunexpected) changes to the cell location information.

The lock component 720, in conjunction with the tag component 718, canfacilitate locking cell location information for a potential celllocation (e.g., estimated cell location information or recorded celllocation information) that has been determined to be and tagged asaccurate based at least in part on the results of the cell locationvalidation. The lock component 720 can lock the cell locationinformation or can indicate, in part via the lock tag, that the celllocation information is to be locked to prevent undesired changes. Ifchanges to the cell location information are desired (e.g., when a cellis removed from a location or moved to another location), the lockcomponent 720, or another component or device, can unlock the celllocation information to facilitate making desired changes to the celllocation information for the cell.

The cell investigation component 722 can initiate a cell investigationof a location of a cell, for example, when the cell location has beentagged as bad, based at least in part on the results of the celllocation validation performed with regard to the cell by the validatorcomponent 712. The cell investigation can include a manual or automatedinvestigation on map or a physical visit to the estimated cell location,recorded cell location, or other potential location of the cell, such asdescribed herein. Based at least in part on the results of the cellinvestigation of the location of a cell, the cell investigationcomponent 722 can be employed to provide update information regardingthe cell location of the cell to the cell location pool component, andthe cell location pool component can update the cell locationinformation for the cell based at least in part on such updateinformation. For instance, if the cell investigation results in anaccurate, or a more accurate or complete, location of a cell, the updateinformation from the cell investigation can include updated celllocation information regarding the accurate, or more accurate orcomplete, cell location.

The cell monitoring request component 724 can initiate a cell monitoringrequest, for example, if a cell location of a cell has been tagged asuncertain, to have the cell monitored for a desired (e.g., longer)period of time to collect data (e.g., additional location-related data)relating to the cell and have further analysis performed on such data(e.g., by the CLC 700). In response to the cell monitoring request, theCLC 700 and/or other components or devices (e.g., base station) canobtain or collect additional data, including location-related data,associated with communication devices that are associated with the cellor associated node (e.g., base station) for a desired period of time.The CLC 700 can analyze the additional data to facilitate estimating thelocation of the cell and validating the location of the cell, inaccordance with the defined cell location management criteria.

The processor component 726 can work in conjunction with the othercomponents (e.g., communicator component 702, data collector component706, estimator component 708, . . . , cell investigation component 722,cell monitoring request component 724, and/or data store 728) tofacilitate performing the various functions of the CLC 700. Theprocessor component 726 can employ one or more processors,microprocessors, or controllers that can process data, such asinformation relating to communication devices, call trace records (e.g.,TA measurement data from call trace records), location data (e.g., AGPSor GPS location data, and/or IoT geolocation data) associated withcommunication devices, network conditions, cell location estimation,cell location validation, metadata, parameters, defined thresholdlevels, rules associated with cell location validation, traffic flows,policies, defined cell location management criteria, algorithms (e.g.,smallTA algorithm, linear regression algorithm, machine learningalgorithms, validation algorithms, etc.), protocols, interfaces, tools,and/or other information, to facilitate operation of the CLC 700, asmore fully disclosed herein, and control data flow between the CLC 700and other components (e.g., a base station or other network component ordevice of the communication network, cell location pool component and/ordata sources, applications, . . . ) associated with the CLC 700.

The data store 728 can store data structures (e.g., user data,metadata), code structure(s) (e.g., modules, objects, hashes, classes,procedures) or instructions, information relating to communicationdevices, call trace records (e.g., TA measurement data from call tracerecords), location data (e.g., AGPS or GPS location data, and/or IoTgeolocation data) associated with communication devices, networkconditions, cell location estimation, cell location validation,metadata, parameters, defined threshold levels, rules associated withcell location validation, traffic flows, policies, defined cell locationmanagement criteria, algorithms (e.g., smallTA algorithm, linearregression algorithm, machine learning algorithms, validationalgorithms, etc.), protocols, interfaces, tools, and/or otherinformation, to facilitate controlling operations associated with theCLC 700. In an aspect, the processor component 726 can be functionallycoupled (e.g., through a memory bus) to the data store 728 in order tostore and retrieve information desired to operate and/or conferfunctionality, at least in part, to the communicator component 702,operations manager component 704, data collector component 706,estimator component 708, validator component 712, lock component 720,cell investigation component 722, cell monitoring request component 724,and data store 728, etc., and/or substantially any other operationalaspects of the CLC 700.

Described herein are systems, methods, articles of manufacture, andother embodiments or implementations that can facilitate estimatinglocations of cells and validating cell locations (e.g., estimated celllocations and/or recorded cell locations from data sources) of cells ofa communication network, as more fully described herein. The estimatinglocations of cells and validating cell locations of cells of acommunication network, and/or other features of the disclosed subjectmatter, can be implemented in connection with any type of device with aconnection to, or attempting to connect to, the communication network(e.g., a wireless or mobile device, a computer, a handheld device,etc.), any Internet of things (IoT) device (e.g., health monitoringdevice, toaster, coffee maker, blinds, music players, speakers, etc.),and/or any connected vehicles (e.g., cars, airplanes, space rockets,and/or other at least partially automated vehicles (e.g., drones)). Insome embodiments, the non-limiting term user equipment (UE) is used. Itcan refer to any type of wireless device that communicates with a radionetwork node in a cellular or mobile communication system. Examples ofUE can be a target device, device to device (D2D) UE, machine type UE orUE capable of machine to machine (M2M) communication, PDA, Tablet,mobile terminals, smart phone, Laptop Embedded Equipped (LEE), laptopmounted equipment (LME), USB dongles, etc. Note that the terms element,elements and antenna ports can be interchangeably used but carry thesame meaning in this disclosure. The embodiments are applicable tosingle carrier as well as to Multi-Carrier (MC) or Carrier Aggregation(CA) operation of the UE. The term Carrier Aggregation (CA) is alsocalled (e.g., interchangeably called) “multi-carrier system,”“multi-cell operation,” “multi-carrier operation,” “multi-carrier”transmission and/or reception.

In some embodiments, the non-limiting term radio network node or simplynetwork node is used. It can refer to any type of network node thatserves one or more UEs and/or that is coupled to other network nodes ornetwork elements or any radio node from where the one or more UEsreceive a signal. Examples of radio network nodes are Node B, BaseStation (BS), Multi-Standard Radio (MSR) node such as MSR BS, eNode B,network controller, Radio Network Controller (RNC), Base StationController (BSC), relay, donor node controlling relay, Base TransceiverStation (BTS), Access Point (AP), transmission points, transmissionnodes, RRU, RRH, nodes in Distributed Antenna System (DAS) etc.

Cloud Radio Access Networks (RAN) can enable the implementation ofconcepts such as software-defined network (SDN) and network functionvirtualization (NFV) in 5G networks. This disclosure can facilitate ageneric channel state information framework design for a 5G network.Certain embodiments of this disclosure can comprise an SDN controllercomponent that can control routing of traffic within the network andbetween the network and traffic destinations. The SDN controllercomponent can be merged with the 5G network architecture to enableservice deliveries via open Application Programming Interfaces (APIs)and move the network core towards an all Internet Protocol (IP), cloudbased, and software driven telecommunications network. The SDNcontroller component can work with, or take the place of Policy andCharging Rules Function (PCRF) network elements so that policies such asquality of service and traffic management and routing can besynchronized and managed end to end.

To meet the huge demand for data centric applications, 4G standards canbe applied to 5G, also called New Radio (NR) access. 5G networks cancomprise the following: data rates of several tens of megabits persecond supported for tens of thousands of users; 1 gigabit per secondcan be offered simultaneously (or concurrently) to tens of workers onthe same office floor; several hundreds of thousands of simultaneous (orconcurrent) connections can be supported for massive sensor deployments;spectral efficiency can be enhanced compared to 4G; improved coverage;enhanced signaling efficiency; and reduced latency compared to LTE. Inmulticarrier system such as OFDM, each subcarrier can occupy bandwidth(e.g., subcarrier spacing). If the carriers use the same bandwidthspacing, then it can be considered a single numerology. However, if thecarriers occupy different bandwidth and/or spacing, then it can beconsidered a multiple numerology.

Referring now to FIG. 8, depicted is an example block diagram of anexample communication device 800 (e.g., wireless or mobile phone,electronic pad or tablet, electronic eyewear, electronic watch, or otherelectronic bodywear, or IoT device, . . . ) operable to engage in asystem architecture that facilitates wireless communications accordingto one or more embodiments described herein. Although a communicationdevice is illustrated herein, it will be understood that other devicescan be a communication device, and that the communication device ismerely illustrated to provide context for the embodiments of the variousembodiments described herein. The following discussion is intended toprovide a brief, general description of an example of a suitableenvironment in which the various embodiments can be implemented. Whilethe description includes a general context of computer-executableinstructions embodied on a machine-readable storage medium, thoseskilled in the art will recognize that the disclosed subject matter alsocan be implemented in combination with other program modules and/or as acombination of hardware and software.

Generally, applications (e.g., program modules) can include routines,programs, components, data structures, etc., that perform particulartasks or implement particular abstract data types. Moreover, thoseskilled in the art will appreciate that the methods described herein canbe practiced with other system configurations, includingsingle-processor or multiprocessor systems, minicomputers, mainframecomputers, as well as personal computers, hand-held computing devices,microprocessor-based or programmable consumer electronics, and the like,each of which can be operatively coupled to one or more associateddevices.

A computing device can typically include a variety of machine-readablemedia. Machine-readable media can be any available media that can beaccessed by the computer and includes both volatile and non-volatilemedia, removable and non-removable media. By way of example and notlimitation, computer-readable media can include computer storage mediaand communication media. Computer storage media can include volatileand/or non-volatile media, removable and/or non-removable mediaimplemented in any method or technology for storage of information, suchas computer-readable instructions, data structures, program modules, orother data. Computer storage media can include, but is not limited to,RAM, ROM, EEPROM, flash memory or other memory technology, solid statedrive (SSD) or other solid-state storage technology, Compact Disk ReadOnly Memory (CD ROM), digital video disk (DVD), Blu-ray disk, or otheroptical disk storage, magnetic cassettes, magnetic tape, magnetic diskstorage or other magnetic storage devices, or any other medium which canbe used to store the desired information and which can be accessed bythe computer. In this regard, the terms “tangible” or “non-transitory”herein as applied to storage, memory or computer-readable media, are tobe understood to exclude only propagating transitory signals per se asmodifiers and do not relinquish rights to all standard storage, memoryor computer-readable media that are not only propagating transitorysignals per se.

Communication media typically embodies computer-readable instructions,data structures, program modules, or other data in a modulated datasignal such as a carrier wave or other transport mechanism, and includesany information delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media includes wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared and other wireless media. Combinations of the anyof the above should also be included within the scope ofcomputer-readable media.

The communication device 800 can include a processor 802 for controllingand processing all onboard operations and functions. A memory 804interfaces to the processor 802 for storage of data and one or moreapplications 806 (e.g., a video player software, user feedback componentsoftware, etc.). Other applications can include voice recognition ofpredetermined voice commands that facilitate initiation of the userfeedback signals. The applications 806 can be stored in the memory 804and/or in a firmware 808, and executed by the processor 802 from eitheror both the memory 804 or/and the firmware 808. The firmware 808 canalso store startup code for execution in initializing the communicationdevice 800. A communication component 810 interfaces to the processor802 to facilitate wired/wireless communication with external systems,e.g., cellular networks, VoIP networks, and so on. Here, thecommunication component 810 can also include a suitable cellulartransceiver 811 (e.g., a GSM transceiver) and/or an unlicensedtransceiver 813 (e.g., Wi-Fi, WiMax) for corresponding signalcommunications. The communication device 800 can be a device such as acellular telephone, a PDA with mobile communications capabilities, andmessaging-centric devices. The communication component 810 alsofacilitates communications reception from terrestrial radio networks(e.g., broadcast), digital satellite radio networks, and Internet-basedradio services networks.

The communication device 800 includes a display 812 for displaying text,images, video, telephony functions (e.g., a Caller ID function), setupfunctions, and for user input. For example, the display 812 can also bereferred to as a “screen” that can accommodate the presentation ofmultimedia content (e.g., music metadata, messages, wallpaper, graphics,etc.). The display 812 can also display videos and can facilitate thegeneration, editing and sharing of video quotes. A serial I/O interface814 is provided in communication with the processor 802 to facilitatewired and/or wireless serial communications (e.g., USB, and/or IEEE1394) through a hardwire connection, and other serial input devices(e.g., a keyboard, keypad, and mouse). This supports updating andtroubleshooting the communication device 800, for example. Audiocapabilities are provided with an audio I/O component 816, which caninclude a speaker for the output of audio signals related to, forexample, indication that the user pressed the proper key or keycombination to initiate the user feedback signal. The audio I/Ocomponent 816 also facilitates the input of audio signals through amicrophone to record data and/or telephony voice data, and for inputtingvoice signals for telephone conversations.

The communication device 800 can include a slot interface 818 foraccommodating a SIC (Subscriber Identity Component) in the form factorof a card Subscriber Identity Module (SIM) or universal SIM 820, andinterfacing the SIM card 820 with the processor 802. However, it is tobe appreciated that the SIM card 820 can be manufactured into thecommunication device 800, and updated by downloading data and software.

The communication device 800 can process IP data traffic through thecommunication component 810 to accommodate IP traffic from an IP networksuch as, for example, the Internet, a corporate intranet, a homenetwork, a person area network, etc., through an ISP or broadband cableprovider. Thus, VoIP traffic can be utilized by the communication device800 and IP-based multimedia content can be received in either an encodedor a decoded format.

A video processing component 822 (e.g., a camera) can be provided fordecoding encoded multimedia content. The video processing component 822can aid in facilitating the generation, editing, and sharing of videoquotes. The communication device 800 also includes a power source 824 inthe form of batteries and/or an AC power subsystem, which power source824 can interface to an external power system or charging equipment (notshown) by a power I/O component 826.

The communication device 800 can also include a video component 830 forprocessing video content received and, for recording and transmittingvideo content. For example, the video component 830 can facilitate thegeneration, editing and sharing of video quotes. A location trackingcomponent 832 facilitates geographically locating the communicationdevice 800. As described hereinabove, this can occur when the userinitiates the feedback signal automatically or manually. A user inputcomponent 834 facilitates the user initiating the quality feedbacksignal. The user input component 834 can also facilitate the generation,editing and sharing of video quotes. The user input component 834 caninclude such conventional input device technologies such as a keypad,keyboard, mouse, stylus pen, and/or touch screen, for example.

Referring again to the applications 806, a hysteresis component 836facilitates the analysis and processing of hysteresis data, which isutilized to determine when to associate with the access point. Asoftware trigger component 838 can be provided that facilitatestriggering of the hysteresis component 836 when the Wi-Fi transceiver813 detects the beacon of the access point. A SIP client 840 enables thecommunication device 800 to support SIP protocols and register thesubscriber with the SIP registrar server. The applications 806 can alsoinclude a client 842 that provides at least the capability of discovery,play and store of multimedia content, for example, music.

The communication device 800, as indicated above related to thecommunication component 810, includes an indoor network radiotransceiver 813 (e.g., Wi-Fi transceiver). This function supports theindoor radio link, such as IEEE 802.11, for the dual-mode GSM device(e.g., communication device 800). The communication device 800 canaccommodate at least satellite radio services through a device (e.g.,handset device) that can combine wireless voice and digital radiochipsets into a single device (e.g., single handheld device).

The aforementioned systems and/or devices have been described withrespect to interaction between several components. It should beappreciated that such systems and components can include thosecomponents or sub-components specified therein, some of the specifiedcomponents or sub-components, and/or additional components.Sub-components could also be implemented as components communicativelycoupled to other components rather than included within parentcomponents. Further yet, one or more components and/or sub-componentsmay be combined into a single component providing aggregatefunctionality. The components may also interact with one or more othercomponents not specifically described herein for the sake of brevity,but known by those of skill in the art.

In view of the example systems and/or devices described herein, examplemethods that can be implemented in accordance with the disclosed subjectmatter can be further appreciated with reference to flowcharts in FIGS.9-13. For purposes of simplicity of explanation, example methodsdisclosed herein are presented and described as a series of acts;however, it is to be understood and appreciated that the disclosedsubject matter is not limited by the order of acts, as some acts mayoccur in different orders and/or concurrently with other acts from thatshown and described herein. For example, a method disclosed herein couldalternatively be represented as a series of interrelated states orevents, such as in a state diagram. Moreover, interaction diagram(s) mayrepresent methods in accordance with the disclosed subject matter whendisparate entities enact disparate portions of the methods. Furthermore,not all illustrated acts may be required to implement a method inaccordance with the subject specification. It should be furtherappreciated that the methods disclosed throughout the subjectspecification are capable of being stored on an article of manufactureto facilitate transporting and transferring such methods to computersfor execution by a processor or for storage in a memory.

FIG. 9 illustrates a flow chart of an example method 900 that canestimate a location of a node (e.g., base station) to facilitateestimating a location of a cell associated with the node, in accordancewith various aspects and embodiments of the disclosed subject matter.The method 900 can be employed by, for example, a system that caninclude the CLC, a processor component (e.g., of or associated with theCLC), and/or a data store (e.g., of or associated with the CLC).

At 902, respective TA measurement data and/or respective location dataassociated with respective communication devices of a group ofcommunication devices can be analyzed, wherein the group ofcommunication devices can be associated with a node, and wherein thenode can be associated with a group of cells that can include a cell.The group of communication devices can be associated with (e.g.,connected to, served by, reporting information to, detected by, orotherwise associated with) a node (e.g., base station) that can compriseor be associated with a group of cells, which can include one or morecells (e.g., co-located cells), such as the cell. The CLC can receivethe respective TA measurement data and/or the respective location dataassociated with the respective communication devices from the respectivecommunication devices or from a network device(s) (e.g., base station orother network device) of the communication network. The CLC can analyzethe respective TA measurement data and/or the respective location dataassociated with the respective communication devices.

At 904, a location of the node can be estimated, based at least in parton a result of analyzing the respective TA measurement data and/or therespective location data, to facilitate estimating a location of thecell. Based at least in part on the result of analyzing the respectiveTA measurement data and/or the respective location data associated withthe respective communication devices, the CLC can estimate the locationof the node to facilitate estimating the location of the cell, as morefully described herein. Using the estimated location of the cell, theCLC also can validate, or at least attempt to validate, the estimatedlocation of the cell or a recorded cell location of the cell, which canbe obtained from a cell location pool or other data source by the CLC,as more fully described herein.

FIG. 10 depicts a flow chart of another example method 1000 that canestimate a location of a node (e.g., base station) to facilitateestimating a location of a cell associated with the node, in accordancewith various aspects and embodiments of the disclosed subject matter.The method 1000 can be employed by, for example, a system that caninclude the CLC, a processor component (e.g., of or associated with theCLC), and/or a data store (e.g., of or associated with the CLC).

At 1002, respective TA measurement data and/or respective location dataassociated with respective communication devices of a group ofcommunication devices can be received, wherein the group ofcommunication devices can be associated with a node, and wherein thenode can be associated with a group of cells that can include a cell.The group of communication devices can be associated with (e.g.,connected to, served by, reporting information to, detected by, orotherwise associated with) a node (e.g., base station). The node caninclude or be associated with a group of cells, which can include one ormore cells, such as the cell.

The CLC can receive (e.g., via and/or associated base stations) therespective TA measurement data and/or the respective location dataassociated with the respective communication devices from the respectivecommunication devices or from a network device(s) of the communicationnetwork. For instance, the CLC can receive respective TA measurementdata associated with some or all of the respective communication devicesfrom a network device(s) of the communication network, whereinrespective call trace records associated with the respectivecommunication devices can include the respective TA measurement data.The CLC also can receive respective location data (e.g., GPS or AGPSlocation data, and/or IoT geolocation data) from some or all of therespective communication devices. The CLC can aggregate or combine therespective TA measurement data and/or the respective location dataassociated with the respective communication devices based at least inpart on respective device identifiers and respective time data (e.g.,timestamp data) associated with the respective communication devices.

At 1004, the respective TA measurement data and/or the respectivelocation data associated with the respective communication devices canbe analyzed. The CLC can analyze the respective TA measurement dataand/or the respective location data associated with the respectivecommunication devices.

At 1006, the number of communication devices of the group ofcommunication devices within a defined threshold distance of the nodecan be determined based at least in part on the results of analyzing therespective TA measurement data associated with the respectivecommunication devices.

At 1008, a determination can be made regarding whether the number ofcommunication devices within the defined threshold distance of the nodesatisfies a defined threshold value (e.g., defined threshold number ofcommunication devices, or defined threshold percentage of communicationdevices of the group of communication devices). The CLC can determinewhether the number of communication devices within the defined thresholddistance of the node (e.g., base station) satisfies (e.g., meets orexceeds) the defined threshold value (e.g., number or percentage),wherein the defined threshold value can be determined and/or set (e.g.,by the CLC) in accordance with the defined cell location managementcriteria.

In response to determining that the number of communication deviceswithin the defined threshold distance of the node satisfies the definedthreshold value, at 1010, the location of the node, and the associatedcell, can be estimated based at least in part on the results ofanalyzing the respective location data associated with the subgroup ofthe respective communication devices that are within the definedthreshold distance of the node. In response to determining that thenumber of communication devices within the defined threshold distance ofthe node satisfies the defined threshold value, the CLC can estimate thelocation of the node, and accordingly, the associated cell, based atleast in part on the results of analyzing the respective location data(e.g., AGPS data, GPS data, or IoT geolocation data) associated with thesubgroup of the respective communication devices that are determined tobe within the defined threshold distance of the node. In someembodiments, based at least in part on the results of analyzing therespective location data of the respective communication devices of thesubgroup of communication devices, the CLC can estimate the location ofthe node and associated cell as a function of the median (or average) ofthe respective locations (e.g., AGPS, GPS, or IoT locations) of therespective communication devices of the subgroup in relation to thenode.

Referring again to reference numeral 1008, if, at 1008, it is determinedthat the number of communication devices within the defined thresholddistance of the node does not satisfy the defined threshold value, at1012, linear regression analysis can be performed on the respective TAmeasurement data associated with the respective communication devices ofthe group of communication devices to facilitate estimating the locationof the node and associated cell. In response to determining that thenumber of communication devices within the defined threshold distance ofthe node does not satisfy the defined threshold value, the CLC canperform the linear regression analysis on the respective TA measurementdata associated with the respective communication devices of the groupof communication devices, in accordance with (e.g., using or applying)the defined linear regression analysis algorithm, to facilitateestimating the location of the node and associated cell, as more fullydescribed herein. In some embodiments, the CLC can utilize machinelearning techniques or algorithms in connection with performing thelinear regression analysis.

At 1014, the location of the node and associated cell can be estimatedbased at least in part on the results of the linear regression analysisperformed on the respective TA measurement data associated with therespective communication devices of the group of communication devices.The CLC can estimate the location of the node, and accordingly, theassociated cell, based at least in part on the results of the linearregression analysis performed on the respective TA measurement dataassociated with the respective communication devices, as more fullydescribed herein.

FIG. 11 presents a flow chart of an example method 1100 that candetermine distance differences between a recorded or estimated locationof a cell and respective locations of respective communication devicesof a group of communication devices associated with the cell, tofacilitate determining a validation status of the recorded locationand/or estimated location of the cell, in accordance with variousaspects and embodiments of the disclosed subject matter. The method 1100can be employed by, for example, a system that can include the CLC, aprocessor component (e.g., of or associated with the CLC), and/or a datastore (e.g., of or associated with the CLC).

At 1102, for each communication device associated with a cell, a firstdistance between the device-reported location of the communicationdevice to a potential location of the cell can be determined based atleast in part on the location data associated with the communicationdevice and the cell location data associated with the potential locationof the cell, wherein the potential location of the cell can be arecorded location of the cell or an estimated location of the cell. Foreach communication device associated with the cell, the CLC candetermine (e.g., calculate) the first distance, D1, between thedevice-reported location of the communication device to the potentiallocation of the cell based at least in part on the location data (e.g.,AGPS or GPS location data) associated with the communication device andthe cell location data associated with the potential location of thecell.

At 1104, for each communication device associated with the cell, asecond distance between the communication device and the true locationof the cell can be determined based at least in part on TA measurementdata associated with the communication device. The TA measurement dataassociated with the communication device can be reported between thecommunication device and the cell at the true cell location. For eachcommunication device associated with the cell, the CLC can determine thesecond distance, D2, between the communication device and the truelocation of the cell as a function of the TA measurement data associatedwith the communication device and a multipath effect value that canrepresent the multipath effect associated with the communication device.

At 1106, for each communication device associated with the cell, adistance difference between the first distance and the second distancecan be determined as a function of the first distance and the seconddifference associated with the communication device and cell. For eachcommunication device associated with the cell, the CLC can determine(e.g., calculate) the distance difference (DD) between the firstdistance and the second distance as a function of the first distance andthe second difference associated with the communication device and cell.For example, the CLC can determine the distance difference as theabsolute value of the difference between the first distance and thesecond distance associated with the communication device and cell. Theabsolute value of the distance difference between the first distance andthe second difference can be less than or equal to the amount of errorin distance between the potential location of the cell (e.g., recordedlocation of the cell or estimated location of the cell) and the truelocation of the cell, wherein such error in distance also can bereferred to as ERR.

At 1108, a validation status of the potential location of the cell canbe determined based at least in part on the respective distancedifferences associated with the respective communication devicesassociated with the cell and a set of rules relating to cell locationvalidation, in accordance with the defined cell location managementcriteria. The CLC can determine the validation status of the potentiallocation of the cell (e.g., recorded location of the cell or estimatedlocation of the cell) based at least in part on the respective distancedifferences associated with the respective communication devicesassociated with the cell and the set of rules, as more fully describedherein. The CLC can determine the validation status based at least inpart on the result of determining which rule, if any, of the rule set issatisfied.

For instance, the CLC can determine (e.g., calculate) the respectiveupper bounds (UBs) of ERR associated with the respective communicationdevices based at least in part on (e.g., as a function of) therespective first distances and the respective TA measurement data (and adefined distance factor) associated with the respective communicationdevices, as more fully described herein. If the CLC determines that therespective UBs of ERR associated with a portion of the respectivecommunication devices in a defined percentile (e.g., a bottom or lowerend percentile, such as 1 percentile, which can be the bottom 1% of theUB values associated with the communication devices, or another desiredpercentile value) satisfy the defined threshold distance (e.g., 150meters, or other desired distance greater than or less than 150 meters)associated with a first rule of the set of rules, the CLC can determinethat the potential (e.g., recorded or estimated) location of the cell isgood, can flag the potential location of the cell as being good, and cantag the potential location of the cell as accurate.

If the CLC determines that one or more of the respective UBs of ERRassociated with the portion of the respective communication devices inthe defined percentile do not satisfy the defined threshold distanceassociated with the first rule, the CLC can determine that furtheranalysis is to be performed to facilitate determining whether thepotential cell location is to be flagged as good and tagged as accurate,is to be flagged as fine and tagged as acceptable, is to be flagged asbad and tagged as unacceptable, or is to be flagged and tagged asuncertain, based at least in part on the set of rules, as more fullydescribed herein.

FIG. 12 illustrates a flow chart of an example method 1200 that candetermine a distance between a recorded location of a cell and anestimated location of the cell to facilitate determining a validationstatus of the recorded location and/or estimated location of the cell,in accordance with various aspects and embodiments of the disclosedsubject matter. The method 1200 can be employed by, for example, asystem that can include the CLC, a processor component (e.g., of orassociated with the CLC), and/or a data store (e.g., of or associatedwith the CLC). The method 1200 can facilitate cross validation of therecorded location and the estimated location of the cell, as well asfacilitate determining a validation status of the recorded locationand/or estimated location of the cell.

The method 1200 can be utilized, for example, in instances where the CLCdetermines that one or more of the respective UBs of ERR associated withthe portion of the respective communication devices in the definedpercentile (e.g., a bottom or lower end percentile, such as 1percentile) do not satisfy the defined threshold distance associatedwith the first rule, and the CLC determines that one or more of therespective distance differences (DDs) associated with a second portionof the respective communication devices in a second defined percentile(e.g., another lower end percentile, such as the 25^(th) percentile) donot satisfy a second defined threshold distance associated with a secondrule of the set of rules, as more fully described herein.

At 1202, a recorded location of a cell can be received from a celllocation pool. The CLC can receive the recorded location of the cellfrom the cell location pool.

At 1204, an estimated location of the cell can be obtained. The CLC canobtain (e.g., retrieve) the estimated location of the cell from a datastore (e.g., when the estimated cell location already has beendetermined) of the CLC or can obtain the estimated location of the cellby determining the estimated location. For instance, the CLC candetermine the estimated location of the cell, as more fully describedherein, and can store the estimated location of the cell in the datastore and/or can subsequently (e.g., immediately or substantiallyimmediately) use the estimated location of the cell to facilitatedetermining a validation status of the recorded location and/orestimated location of the cell.

At 1206, the distance between the recorded location and the estimatedlocation of the cell can be determined based at least in part on theresults of analyzing the recorded location and the estimated location ofthe cell. The CLC can analyze the recorded location of the cell and theestimated location of the cell. Based at least in part on the results ofthe analysis, the CLC can determine (e.g., calculate) the distancebetween the recorded location of the cell and the estimated location ofthe cell (the D_RE).

At 1208, a determination can be made regarding whether the distancebetween the recorded location and the estimated location of the cellsatisfies a defined threshold distance. The CLC can determine whetherthe distance between the recorded location and the estimated location ofthe cell satisfies (e.g., is equal to or less than) the definedthreshold distance based at least in part on the result of comparing thedistance to the defined threshold distance, wherein a defined rule(e.g., first rule of the set of rules) can specify the defined thresholddistance to be applied. The CLC can determine or set the definedthreshold distance (e.g., defined threshold D_RE) based at least in parton the defined cell location management criteria (e.g., the defined rulespecified by the defined cell location management criteria). In someembodiments, the defined threshold distance can be 150 meters, and, inother embodiments, the defined threshold distance can be less than ormore than 150 meters, as indicated or specified by the defined celllocation management criteria.

In response to determining that the distance between the recordedlocation and the estimated location of the cell satisfies the definedthreshold distance, at 1210, the recorded location and the estimatedlocation can be determined to be, and flagged as, good, and the recordedlocation and/or estimated location can be tagged as accurate. Inresponse to determining that the distance between the recorded locationand the estimated location of the cell satisfies the defined thresholddistance, the CLC can determine that the recorded location and theestimated location are good, can flag the recorded location and/orestimated location as being good, and can tag the recorded locationand/or estimated location as accurate.

Referring again to reference numeral 1208, if, at 1208, it is determinedthat the distance between the recorded location and the estimatedlocation of the cell does not satisfy (e.g., exceeds) the definedthreshold distance, at 1212, the recorded location or the estimatedlocation can be determined to be, and flagged as, fine, and the recordedlocation or estimated location can be tagged as acceptable. In responseto determining that the distance between the recorded location and theestimated location of the cell does not satisfy the defined thresholddistance, the CLC can determine that the recorded location or theestimated location is fine, and is to be flagged as fine, and therecorded location or estimated location can be tagged as acceptable, asmore fully described herein. With regard to multiple potential celllocations being evaluated, if more than one potential cell location isable to be flagged as fine, the CLC can determine the best (e.g., moreaccurate) cell location of those potential cell locations, and can flagthe best cell location as fine and tag that best cell location asacceptable, wherein the best cell location can be associated with thebest data source, which can be the estimator component and its estimatedcell location or a data source associated with a recorded data source.

FIG. 13 illustrates a flow chart of an example method 1300 that candetermine a validation status of a recorded location and/or an estimatedlocation of a cell based at least in part on the distance between therecorded location and the estimated location, and/or distancedifferences between the recorded and/or estimated location of the celland respective locations of respective communication devices of a groupof communication devices associated with the cell, to facilitatedetermining a validation status of the recorded location and/orestimated location of the cell, in accordance with various aspects andembodiments of the disclosed subject matter. The method 1300 can beemployed by, for example, a system that can include the CLC, a processorcomponent (e.g., of or associated with the CLC), and/or a data store(e.g., of or associated with the CLC).

At 1302, a determination can be made regarding whether respective UBs ofERR associated with a first portion of the respective communicationdevices in a first defined percentile satisfy the first definedthreshold distance associated with a first rule of a set of rules. TheCLC can determine (e.g., calculate) the respective UBs of ERR associatedwith the respective communication devices based at least in part on(e.g., as a function of) respective first distances and respective TAmeasurement data (and a defined distance factor, such as, e.g., 78 m)associated with the respective communication devices, as more fullydescribed herein. The CLC can determine whether the respective UBs ofERR associated with the first portion of the respective communicationdevices in the first defined percentile (e.g., a bottom or lower endpercentile, such as 1 percentile, or another desired percentile value)satisfy the first defined threshold distance associated with the firstrule.

In response to determining that the respective UBs of ERR associatedwith the first portion of the respective communication devices in thefirst defined percentile satisfy the first defined threshold distanceassociated with the first rule, at 1304, the potential (e.g., recordedor estimated) location of the cell can be determined to be, and can beflagged as, good, and the potential location can be tagged as accurate.If the CLC determines that the respective UBs of ERR associated with thefirst portion of the respective communication devices in the firstdefined percentile satisfy the defined threshold distance associatedwith the first rule, the CLC can determine that the potential (e.g.,recorded or estimated) location of the cell is good, can flag thepotential location of the cell as being good, and can tag the potentiallocation of the cell as accurate. Accordingly, the potential location ofthe cell can have a validation status of accurate.

Referring again to reference numeral 1302, if, at 1302, it is determinedthat one or more of the respective UBs of ERR associated with the firstportion of the respective communication devices in the first definedpercentile do not satisfy the first defined threshold distanceassociated with the first rule, at 1306, a determination can be madethat further analysis is to be performed to facilitate determining avalidation status of the recorded location and/or estimated location ofthe cell. If the CLC determines that one or more of the respective UBsof ERR associated with the first portion of the respective communicationdevices in the first defined percentile do not satisfy the first definedthreshold distance associated with the first rule, the CLC can determinethat further analysis is to be performed to facilitate determining avalidation status of the recorded location and/or estimated location ofthe cell based at least in part on the set of rules.

At 1308, a determination can be made regarding whether respectivedistance differences (DDs) associated with a second portion of therespective communication devices in a second defined percentile satisfya second defined threshold distance associated with a second rule of theset of rules. The CLC can analyze (e.g., compare) the respectivedistance differences associated with the second portion of therespective communication devices in a second defined percentile (e.g.,another lower end percentile, such as the 25^(th) percentile, which canbe the bottom 25% of the respective distance differences associated withthe respective communication devices, or other desired percentile value)satisfy the second defined threshold distance associated with the secondrule. Based at least in part on the analysis results, the CLC candetermine whether the respective distance differences associated withthe second portion of the respective communication devices in the seconddefined percentile satisfy (e.g., are less than or equal to) the seconddefined threshold distance associated with the second rule. Inaccordance with various embodiments, the second defined thresholddistance can be the same as the first defined threshold distance (e.g.,150 meters (or other desired distance)) or different from the firstdefined threshold distance, as indicated or specified by the definedcell location management criteria.

In response to determining that the respective distance differencesassociated with the second portion of the respective communicationdevices in the second defined percentile satisfy the second definedthreshold distance associated with the second rule, at 1310, adetermination can be made regarding whether the distance between therecorded location of a cell and the estimated location of the cell(D_RE) satisfies a first defined threshold distance associated with thefirst rule. To facilitate determining the validation status of thepotential cell location, if the CLC determines that the respectivedistance differences associated with the second portion of therespective communication devices in the second defined percentilesatisfy the second defined threshold distance associated with the secondrule, the CLC can determine whether the distance between the recordedlocation of the cell and the estimated location of the cell (D_RE)satisfies the first defined threshold distance associated with the firstrule. For example, the CLC can compare the distance between the recordedlocation and the estimated location of the cell to the first definedthreshold distance (e.g., 150 meters, or other desired distance greaterthan or less than 150 meters), which can be indicated or specified bythe first rule, in accordance with the defined cell location managementcriteria. Based at least in part on the results of the comparison, theCLC can determine whether the distance between the recorded location andthe estimated location of the cell satisfies the first defined thresholddistance associated with the first rule.

If it is determined that the distance between the recorded location ofthe cell and the estimated location of the cell (D_RE) satisfies thefirst defined threshold distance associated with the first rule, themethod 1300 can proceed from reference numeral 1310 to reference numeral1304, wherein the potential (e.g., recorded or estimated) location ofthe cell can be determined to be, and can be flagged as, good, and thepotential location can be tagged as accurate. For instance, if the CLCdetermines that the distance between the recorded location of the celland the estimated location of the cell (D_RE) satisfies the firstdefined threshold distance associated with the first rule, the CLC candetermine that the potential (e.g., recorded or estimated) location ofthe cell is good, can flag the potential location of the cell as beinggood, and can tag the potential location of the cell as accurate.Accordingly, the potential location of the cell can have a validationstatus of accurate.

Referring again to reference numeral 1310, if, at 1310, it is determinedthat the distance between the recorded location and the estimatedlocation of the cell (DRE) does not satisfy (e.g., exceeds) the firstdefined threshold distance associated with the first rule, at 1312, thepotential (e.g., recorded or estimated) location of the cell can bedetermined to be, and can be flagged as, fine, and the potentiallocation can be tagged as acceptable. If the CLC determines that thedistance between the recorded location and the estimated location of thecell does not satisfy the first defined threshold distance, the CLC candetermine that the potential (e.g., recorded or estimated) location ofthe cell is fine, can flag the potential location of the cell as beingfine, and can tag the potential location of the cell as acceptable. Withregard to multiple potential cell locations being evaluated, if morethan one potential cell location is able to be flagged as fine, the CLCcan determine the best (e.g., more accurate) cell location of thosepotential cell locations, and can flag the best cell location as fineand tag that best cell location as acceptable, wherein the best celllocation can be associated with the best data source, which can be theestimator component and its estimated cell location or a data sourceassociated with a recorded data source. Accordingly, the potentiallocation of the cell can have a validation status of acceptable.

Referring again to reference numeral 1308, if, at 1308, it is determinedthat one or more of the respective distance differences associated withthe second portion of the respective communication devices in the seconddefined percentile do not satisfy the second defined threshold distanceassociated with the second rule, at 1314, it can be determined that thepotential location of the cell is not to be flagged as good or fine andis not to be tagged as accurate or acceptable. If the CLC determinesthat one or more of the respective distance differences associated withthe second portion of the respective communication devices in the seconddefined percentile do not satisfy the second defined threshold distanceassociated with the second rule, the CLC can determine that thepotential location of the cell is not to be labeled or flagged as goodor fine and is not to be tagged as accurate or acceptable. Whendetermining whether to flag a potential cell location associated withthe cell as fine (after determining that no potential cell locationassociated with the cell is to be flagged as good), the CLC can performsuch cell location validation with regard to each of the potential celllocations of a cell to determine whether any of the potential celllocations can be flagged as fine. The CLC also can determine thatfurther analysis is to be performed to facilitate determining whetherthe potential cell location is to be flagged as bad and tagged asunacceptable, or is to be flagged and tagged as uncertain, based atleast in part on another rule (e.g., third rule) of the set of rules.

At 1316, a determination can be made regarding whether the number ofcommunication devices in the second portion of communication devices inthe second defined percentile satisfies a defined threshold number ofcommunication devices associated with a third rule of the set of rules.The CLC can determine the number of communication devices in the secondportion of communication devices in the second defined percentile, basedat least in part on the analysis results. Further, based at least inpart on the analysis results, the CLC can determine whether the numberof communication devices in the second portion of communication devicesin the second defined percentile satisfies (e.g., meets or exceeds) thedefined threshold number (e.g., 30 or other desired number less than orgreater than 30) of communication devices associated with the thirdrule. The defined threshold number can be indicated or specified by thedefined cell location management criteria.

In response to determining that the number of communication devices inthe second portion of communication devices in the second definedpercentile satisfies the defined threshold number associated with thethird rule, at 1318, the potential (e.g., recorded or estimated)location of the cell can be determined to be bad, and the potentiallocation can be flagged and tagged as bad. If the CLC determines thatthe respective distance differences associated with the second portionof the respective communication devices in the second defined percentilesatisfy the second defined threshold distance associated with the secondrule, the CLC can determine that the potential (e.g., recorded orestimated) location of the cell is bad or unacceptable, can flag and tagthe potential location of the cell as being bad or unacceptable.Accordingly, the potential location of the cell can have a validationstatus of bad or unacceptable.

Referring again to reference numeral 1316, if, at 1316, it is determinedthat the number of communication devices in the second portion ofcommunication devices in the second defined percentile does not satisfythe defined threshold number associated with the third rule, at 1320, itcan be determined that the potential location of the cell is to beflagged and tagged as uncertain. If the CLC determines that the numberof communication devices in the second portion of communication devicesin the second defined percentile does not satisfy (e.g., is not greaterthan or equal to) the defined threshold number associated with the thirdrule, the CLC can determine that the potential location of the cell isto be flagged and tagged as uncertain. Accordingly, the potentiallocation of the cell can have a validation status of uncertain.

In order to provide additional context for various embodiments describedherein, FIG. 14 and the following discussion are intended to provide abrief, general description of a suitable computing environment 1400 inwhich the various embodiments of the embodiments described herein can beimplemented. While the embodiments have been described above in thegeneral context of computer-executable instructions that can run on oneor more computers, those skilled in the art will recognize that theembodiments can be also implemented in combination with other programmodules and/or as a combination of hardware and software.

Generally, program modules include routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the methods can be practiced with other computer systemconfigurations, including single-processor or multiprocessor computersystems, minicomputers, mainframe computers, Internet of Things (IoT)devices, distributed computing systems, as well as personal computers,hand-held computing devices, microprocessor-based or programmableconsumer electronics, and the like, each of which can be operativelycoupled to one or more associated devices.

The illustrated embodiments of the embodiments herein can be alsopracticed in distributed computing environments where certain tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules can be located in both local and remote memory storage devices.

Computing devices typically include a variety of media, which caninclude computer-readable storage media, machine-readable storage media,and/or communications media, which two terms are used herein differentlyfrom one another as follows. Computer-readable storage media ormachine-readable storage media can be any available storage media thatcan be accessed by the computer and includes both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media or machine-readablestorage media can be implemented in connection with any method ortechnology for storage of information such as computer-readable ormachine-readable instructions, program modules, structured data orunstructured data.

Computer-readable storage media can include, but are not limited to,random access memory (RAM), read only memory (ROM), electricallyerasable programmable read only memory (EEPROM), flash memory or othermemory technology, compact disk read only memory (CD-ROM), digitalversatile disk (DVD), Blu-ray disc (BD) or other optical disk storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, solid state drives or other solid statestorage devices, or other tangible and/or non-transitory media which canbe used to store desired information. In this regard, the terms“tangible” or “non-transitory” herein as applied to storage, memory orcomputer-readable media, are to be understood to exclude onlypropagating transitory signals per se as modifiers and do not relinquishrights to all standard storage, memory or computer-readable media thatare not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and includes any information deliveryor transport media. The term “modulated data signal” or signals refersto a signal that has one or more of its characteristics set or changedin such a manner as to encode information in one or more signals. By wayof example, and not limitation, communication media include wired media,such as a wired network or direct-wired connection, and wireless mediasuch as acoustic, RF, infrared and other wireless media.

With reference again to FIG. 14, the example environment 1400 forimplementing various embodiments of the aspects described hereinincludes a computer 1402, the computer 1402 including a processing unit1404, a system memory 1406 and a system bus 1408. The system bus 1408couples system components including, but not limited to, the systemmemory 1406 to the processing unit 1404. The processing unit 1404 can beany of various commercially available processors. Dual microprocessorsand other multi-processor architectures can also be employed as theprocessing unit 1404.

The system bus 1408 can be any of several types of bus structure thatcan further interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 1406includes ROM 1410 and RAM 1412. A basic input/output system (BIOS) canbe stored in a non-volatile memory such as ROM, erasable programmableread only memory (EPROM), EEPROM, which BIOS contains the basic routinesthat help to transfer information between elements within the computer1402, such as during startup. The RAM 1412 can also include a high-speedRAM such as static RAM for caching data.

The computer 1402 further includes an internal hard disk drive (HDD)1414 (e.g., EIDE, SATA), one or more external storage devices 1416(e.g., a magnetic floppy disk drive (FDD) 1416, a memory stick or flashdrive reader, a memory card reader, etc.) and an optical disk drive 1420(e.g., which can read or write from a CD-ROM disc, a DVD, a BD, etc.).While the internal HDD 1414 is illustrated as located within thecomputer 1402, the internal HDD 1414 can also be configured for externaluse in a suitable chassis (not shown). Additionally, while not shown inenvironment 1400, a solid state drive (SSD) could be used in additionto, or in place of, an HDD 1414. The HDD 1414, external storagedevice(s) 1416 and optical disk drive 1420 can be connected to thesystem bus 1408 by an HDD interface 1424, an external storage interface1426 and an optical drive interface 1428, respectively. The interface1424 for external drive implementations can include at least one or bothof Universal Serial Bus (USB) and Institute of Electrical andElectronics Engineers (IEEE) 1394 interface technologies. Other externaldrive connection technologies are within contemplation of theembodiments described herein.

The drives and their associated computer-readable storage media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 1402, the drives andstorage media accommodate the storage of any data in a suitable digitalformat. Although the description of computer-readable storage mediaabove refers to respective types of storage devices, it should beappreciated by those skilled in the art that other types of storagemedia which are readable by a computer, whether presently existing ordeveloped in the future, could also be used in the example operatingenvironment, and further, that any such storage media can containcomputer-executable instructions for performing the methods describedherein.

A number of program modules can be stored in the drives and RAM 1412,including an operating system 1430, one or more application programs1432, other program modules 1434 and program data 1436. All or portionsof the operating system, applications, modules, and/or data can also becached in the RAM 1412. The systems and methods described herein can beimplemented utilizing various commercially available operating systemsor combinations of operating systems.

Computer 1402 can optionally comprise emulation technologies. Forexample, a hypervisor (not shown) or other intermediary can emulate ahardware environment for operating system 1430, and the emulatedhardware can optionally be different from the hardware illustrated inFIG. 14. In such an embodiment, operating system 1430 can comprise onevirtual machine (VM) of multiple VMs hosted at computer 1402.Furthermore, operating system 1430 can provide runtime environments,such as the Java runtime environment or the .NET framework, forapplications 1432. Runtime environments are consistent executionenvironments that allow applications 1432 to run on any operating systemthat includes the runtime environment. Similarly, operating system 1430can support containers, and applications 1432 can be in the form ofcontainers, which are lightweight, standalone, executable packages ofsoftware that include, e.g., code, runtime, system tools, systemlibraries and settings for an application.

Further, computer 1402 can be enable with a security module, such as atrusted processing module (TPM). For instance with a TPM, bootcomponents hash next in time boot components, and wait for a match ofresults to secured values, before loading a next boot component. Thisprocess can take place at any layer in the code execution stack ofcomputer 1402, e.g., applied at the application execution level or atthe operating system (OS) kernel level, thereby enabling security at anylevel of code execution.

A user can enter commands and information into the computer 1402 throughone or more wired/wireless input devices, e.g., a keyboard 1438, a touchscreen 1440, and a pointing device, such as a mouse 1442. Other inputdevices (not shown) can include a microphone, an infrared (IR) remotecontrol, a radio frequency (RF) remote control, or other remote control,a joystick, a virtual reality controller and/or virtual reality headset,a game pad, a stylus pen, an image input device, e.g., camera(s), agesture sensor input device, a vision movement sensor input device, anemotion or facial detection device, a biometric input device, e.g.,fingerprint or iris scanner, or the like. These and other input devicesare often connected to the processing unit 1404 through an input deviceinterface 1444 that can be coupled to the system bus 1408, but can beconnected by other interfaces, such as a parallel port, an IEEE 1394serial port, a game port, a USB port, an IR interface, a BLUETOOTH™interface, etc.

A monitor 1446 or other type of display device can be also connected tothe system bus 1408 via an interface, such as a video adapter 1448. Inaddition to the monitor 1446, a computer typically includes otherperipheral output devices (not shown), such as speakers, printers, etc.

The computer 1402 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 1450. The remotecomputer(s) 1450 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallyincludes many or all of the elements described relative to the computer1402, although, for purposes of brevity, only a memory/storage device1452 is illustrated. The logical connections depicted includewired/wireless connectivity to a local area network (LAN) 1454 and/orlarger networks, e.g., a wide area network (WAN) 1456. Such LAN and WANnetworking environments are commonplace in offices and companies, andfacilitate enterprise-wide computer networks, such as intranets, all ofwhich can connect to a global communications network, e.g., theInternet.

When used in a LAN networking environment, the computer 1402 can beconnected to the local network 1454 through a wired and/or wirelesscommunication network interface or adapter 1458. The adapter 1458 canfacilitate wired or wireless communication to the LAN 1454, which canalso include a wireless access point (AP) disposed thereon forcommunicating with the adapter 1458 in a wireless mode.

When used in a WAN networking environment, the computer 1402 can includea modem 1460 or can be connected to a communications server on the WAN1456 via other means for establishing communications over the WAN 1456,such as by way of the Internet. The modem 1460, which can be internal orexternal and a wired or wireless device, can be connected to the systembus 1408 via the input device interface 1444. In a networkedenvironment, program modules depicted relative to the computer 1402 orportions thereof, can be stored in the remote memory/storage device1452. It will be appreciated that the network connections shown areexample and other means of establishing a communications link betweenthe computers can be used.

When used in either a LAN or WAN networking environment, the computer1402 can access cloud storage systems or other network-based storagesystems in addition to, or in place of, external storage devices 1416 asdescribed above. Generally, a connection between the computer 1402 and acloud storage system can be established over a LAN 1454 or WAN 1456,e.g., by the adapter 1458 or modem 1460, respectively. Upon connectingthe computer 1402 to an associated cloud storage system, the externalstorage interface 1426 can, with the aid of the adapter 1458 and/ormodem 1460, manage storage provided by the cloud storage system as itwould other types of external storage. For instance, the externalstorage interface 1426 can be configured to provide access to cloudstorage sources as if those sources were physically connected to thecomputer 1402.

The computer 1402 can be operable to communicate with any wirelessdevices or entities operatively disposed in wireless communication,e.g., a printer, scanner, desktop and/or portable computer, portabledata assistant, communications satellite, any piece of equipment orlocation associated with a wirelessly detectable tag (e.g., a kiosk,news stand, store shelf, etc.), and telephone. This can include WirelessFidelity (Wi-Fi) and BLUETOOTH™ wireless technologies. Thus, thecommunication can be a predefined structure as with a conventionalnetwork or simply an ad hoc communication between at least two devices.

Wi-Fi, or Wireless Fidelity, allows connection to the Internet from acouch at home, in a hotel room, or a conference room at work, withoutwires. Wi-Fi is a wireless technology similar to that used in a cellphone that enables such devices, e.g., computers, to send and receivedata indoors and out; anywhere within the range of a base station. Wi-Finetworks use radio technologies called IEEE 802.11 (a, b, g, etc.) toprovide secure, reliable, fast wireless connectivity. A Wi-Fi networkcan be used to connect computers to each other, to the Internet, and towired networks (which use IEEE 802.3 or Ethernet). Wi-Fi networksoperate in the unlicensed 2.4 and 5 GHz radio bands, at an 11 Mbps(802.11a) or 54 Mbps (802.11b) data rate, for example, or with productsthat contain both bands (dual band), so the networks can providereal-world performance similar to the basic 10BaseT wired Ethernetnetworks used in many offices.

It is to be noted that aspects, features, and/or advantages of thedisclosed subject matter can be exploited in substantially any wirelesstelecommunication or radio technology, e.g., Wi-Fi; Gi-Fi; Hi-Fi;BLUETOOTH™; worldwide interoperability for microwave access (WiMAX);enhanced general packet radio service (enhanced GPRS); third generationpartnership project (3GPP) long term evolution (LTE); third generationpartnership project 2 (3GPP2) ultra mobile broadband (UMB); 3GPPuniversal mobile telecommunication system (UMTS); high speed packetaccess (HSPA); high speed downlink packet access (HSDPA); high speeduplink packet access (HSUPA); GSM (global system for mobilecommunications) EDGE (enhanced data rates for GSM evolution) radioaccess network (GERAN); UMTS terrestrial radio access network (UTRAN);LTE advanced (LTE-A); etc. Additionally, some or all of the aspectsdescribed herein can be exploited in legacy telecommunicationtechnologies, e.g., GSM. In addition, mobile as well non-mobile networks(e.g., the internet, data service network such as internet protocoltelevision (IPTV), etc.) can exploit aspects or features describedherein.

Various aspects or features described herein can be implemented as amethod, apparatus, system, or article of manufacture using standardprogramming or engineering techniques. In addition, various aspects orfeatures disclosed in the subject specification can also be realizedthrough program modules that implement at least one or more of themethods disclosed herein, the program modules being stored in a memoryand executed by at least a processor. Other combinations of hardware andsoftware or hardware and firmware can enable or implement aspectsdescribed herein, including disclosed method(s). The term “article ofmanufacture” as used herein is intended to encompass a computer programaccessible from any computer-readable device, carrier, or storage media.For example, computer-readable storage media can include but are notlimited to magnetic storage devices (e.g., hard disk, floppy disk,magnetic strips, etc.), optical discs (e.g., compact disc (CD), digitalversatile disc (DVD), blu-ray disc (BD), etc.), smart cards, and memorydevices comprising volatile memory and/or non-volatile memory (e.g.,flash memory devices, such as, for example, card, stick, key drive,etc.), or the like. In accordance with various implementations,computer-readable storage media can be non-transitory computer-readablestorage media and/or a computer-readable storage device can comprisecomputer-readable storage media.

As it is employed in the subject specification, the term “processor” canrefer to substantially any computing processing unit or devicecomprising, but not limited to, single-core processors;single-processors with software multithread execution capability;multi-core processors; multi-core processors with software multithreadexecution capability; multi-core processors with hardware multithreadtechnology; parallel platforms; and parallel platforms with distributedshared memory. A processor can be or can comprise, for example, multipleprocessors that can include distributed processors or parallelprocessors in a single machine or multiple machines. Additionally, aprocessor can comprise or refer to an integrated circuit, an applicationspecific integrated circuit (ASIC), a digital signal processor (DSP), aprogrammable gate array (PGA), a field PGA (FPGA), a programmable logiccontroller (PLC), a complex programmable logic device (CPLD), a statemachine, a discrete gate or transistor logic, discrete hardwarecomponents, or any combination thereof designed to perform the functionsdescribed herein. Further, processors can exploit nano-scalearchitectures such as, but not limited to, molecular and quantum-dotbased transistors, switches and gates, in order to optimize space usageor enhance performance of user equipment. A processor may also beimplemented as a combination of computing processing units.

A processor can facilitate performing various types of operations, forexample, by executing computer-executable instructions. When a processorexecutes instructions to perform operations, this can include theprocessor performing (e.g., directly performing) the operations and/orthe processor indirectly performing operations, for example, byfacilitating (e.g., facilitating operation of), directing, controlling,or cooperating with one or more other devices or components to performthe operations. In some implementations, a memory can storecomputer-executable instructions, and a processor can be communicativelycoupled to the memory, wherein the processor can access or retrievecomputer-executable instructions from the memory and can facilitateexecution of the computer-executable instructions to perform operations.

In certain implementations, a processor can be or can comprise one ormore processors that can be utilized in supporting a virtualizedcomputing environment or virtualized processing environment. Thevirtualized computing environment may support one or more virtualmachines representing computers, servers, or other computing devices. Insuch virtualized virtual machines, components such as processors andstorage devices may be virtualized or logically represented.

In the subject specification, terms such as “store,” “storage,” “datastore,” data storage,” “database,” and substantially any otherinformation storage component relevant to operation and functionality ofa component are utilized to refer to “memory components,” entitiesembodied in a “memory,” or components comprising a memory. It is to beappreciated that memory and/or memory components described herein can beeither volatile memory or nonvolatile memory, or can include bothvolatile and nonvolatile memory.

By way of illustration, and not limitation, nonvolatile memory caninclude read only memory (ROM), programmable ROM (PROM), electricallyprogrammable ROM (EPROM), electrically erasable ROM (EEPROM), or flashmemory. Volatile memory can include random access memory (RAM), whichacts as external cache memory. By way of illustration and notlimitation, RAM is available in many forms such as synchronous RAM(SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rateSDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), Synchlink DRAM (SLDRAM), anddirect Rambus RAM (DRRAM). Additionally, the disclosed memory componentsof systems or methods herein are intended to comprise, without beinglimited to comprising, these and any other suitable types of memory.

As used in this application, the terms “component”, “system”,“platform”, “framework”, “layer”, “interface”, “agent”, and the like,can refer to and/or can include a computer-related entity or an entityrelated to an operational machine with one or more specificfunctionalities. The entities disclosed herein can be either hardware, acombination of hardware and software, software, or software inexecution. For example, a component may be, but is not limited to being,a process running on a processor, a processor, an object, an executable,a thread of execution, a program, and/or a computer. By way ofillustration, both an application running on a server and the server canbe a component. One or more components may reside within a processand/or thread of execution and a component may be localized on onecomputer and/or distributed between two or more computers.

In another example, respective components can execute from variouscomputer readable media having various data structures stored thereon.The components may communicate via local and/or remote processes such asin accordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, and/or across a network such as the Internet withother systems via the signal). As another example, a component can be anapparatus with specific functionality provided by mechanical partsoperated by electric or electronic circuitry, which is operated by asoftware or firmware application executed by a processor. In such acase, the processor can be internal or external to the apparatus and canexecute at least a part of the software or firmware application. As yetanother example, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,wherein the electronic components can include a processor or other meansto execute software or firmware that confers at least in part thefunctionality of the electronic components. In an aspect, a componentcan emulate an electronic component via a virtual machine, e.g., withina cloud computing system.

In addition, the term “or” is intended to mean an inclusive “or” ratherthan an exclusive “or.” That is, unless specified otherwise, or clearfrom context, “X employs A or B” is intended to mean any of the naturalinclusive permutations. That is, if X employs A; X employs B; or Xemploys both A and B, then “X employs A or B” is satisfied under any ofthe foregoing instances. Moreover, articles “a” and “an” as used in thesubject specification and annexed drawings should generally be construedto mean “one or more” unless specified otherwise or clear from contextto be directed to a singular form.

Moreover, terms like “user equipment” (UE), “mobile station,” “mobile,”“wireless device,” “wireless communication device,” “subscriberstation,” “subscriber equipment,” “access terminal,” “terminal,”“handset,” and similar terminology are used herein to refer to awireless device utilized by a subscriber or user of a wirelesscommunication service to receive or convey data, control, voice, video,sound, gaming, or substantially any data-stream or signaling-stream. Theforegoing terms are utilized interchangeably in the subjectspecification and related drawings. Likewise, the terms “access point”(AP), “base station,” “node B,” “evolved node B” (eNode B or eNB), “homenode B” (HNB), “home access point” (HAP), and the like are utilizedinterchangeably in the subject application, and refer to a wirelessnetwork component or appliance that serves and receives data, control,voice, video, sound, gaming, or substantially any data-stream orsignaling-stream from a set of subscriber stations. Data and signalingstreams can be packetized or frame-based flows.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer,”“owner,” “agent,” and the like are employed interchangeably throughoutthe subject specification, unless context warrants particulardistinction(s) among the terms. It should be appreciated that such termscan refer to human entities or automated components supported throughartificial intelligence (e.g., a capacity to make inference based oncomplex mathematical formalisms), which can provide simulated vision,sound recognition and so forth.

As used herein, the terms “example,” “exemplary,” and/or “demonstrative”are utilized to mean serving as an example, instance, or illustration.For the avoidance of doubt, the subject matter disclosed herein is notlimited by such examples. In addition, any aspect or design describedherein as an “example,” “exemplary,” and/or “demonstrative” is notnecessarily to be construed as preferred or advantageous over otheraspects or designs, nor is it meant to preclude equivalent exemplarystructures and techniques known to those of ordinary skill in the art.Furthermore, to the extent that the terms “includes,” “has,” “contains,”and other similar words are used in either the detailed description orthe claims, such terms are intended to be inclusive, in a manner similarto the term “comprising” as an open transition word, without precludingany additional or other elements.

It is to be appreciated and understood that components (e.g.,communication device, base station, cell, RAN, communication network,cell location component, estimator component, validator component,machine learning component, processor component, data store, . . . ), asdescribed with regard to a particular system or method, can include thesame or similar functionality as respective components (e.g.,respectively named components or similarly named components) asdescribed with regard to other systems or methods disclosed herein.

What has been described above includes examples of systems and methodsthat provide advantages of the disclosed subject matter. It is, ofcourse, not possible to describe every conceivable combination ofcomponents or methods for purposes of describing the disclosed subjectmatter, but one of ordinary skill in the art may recognize that manyfurther combinations and permutations of the disclosed subject matterare possible. Furthermore, to the extent that the terms “includes,”“has,” “possesses,” and the like are used in the detailed description,claims, appendices and drawings such terms are intended to be inclusivein a manner similar to the term “comprising” as “comprising” isinterpreted when employed as a transitional word in a claim.

What is claimed is:
 1. A method, comprising: analyzing, by a systemcomprising a processor, respective timing advance measurement dataassociated with respective devices of a first group of devices andrespective location data associated with the respective devices, whereinthe first group of devices is associated with a base station, andwherein the base station is associated with a second group of cellscomprising a cell; estimating, by the system, a first location of thebase station device, based on a first result of analyzing the respectivetiming advance measurement data and the respective location data, tofacilitate estimating a second location of the cell; and in connectionwith the estimating, determining, by the system, an estimation processof a group of estimation processes to utilize to facilitate theestimating of the first location of the base station, wherein the groupof estimation processes comprises a linear regression analysis processand a different estimation process, wherein the determining of theestimation process comprises determining whether to utilize the linearregression analysis process to facilitate the estimating of the firstlocation of the base station based on a second result of determiningwhether respective distances between the respective devices and the basestation satisfy a defined threshold distance value.
 2. The method ofclaim 1, wherein the different estimation process is a timing advancemeasurement process, and wherein the method further comprises: based ona third result of analyzing the respective timing advance measurementdata associated with the respective devices, determining, by the system,whether the respective distances between the respective devices and thebase station satisfy the defined threshold distance value, wherein therespective timing advance measurement data indicates the respectivedistances between the respective devices and the base station; and basedon the second result of determining whether the respective distancesbetween the respective devices and the base station satisfy the definedthreshold distance value, determining, by the system, whether to utilizethe timing advance measurement process or the linear regression analysisprocess to facilitate the estimating of the first location of the basestation.
 3. The method of claim 2, further comprising: based on thesecond result indicating that an insufficient number of the respectivedistances between the respective devices and the base station satisfythe defined threshold distance value, determining, by the system, thatthe linear regression analysis process is to be utilized to facilitatethe estimating of the first location of the base station; andperforming, by the system, a linear regression analysis on therespective timing advance measurement data associated with therespective devices, in accordance with the linear regression analysisprocess, wherein the estimating of the first location of the basestation comprises estimating the first location of the base stationbased on a fourth result of the linear regression analysis.
 4. Themethod of claim 2, further comprising: based on the second resultindicating that a sufficient number of the respective distances betweenthe respective devices and the base station satisfy the definedthreshold distance value, determining, by the system, that the timingadvance measurement process is to be utilized to facilitate theestimating of the first location of the base station; and determining,by the system, a median distance between the base station and therespective devices based on the respective location data, in accordancewith the timing advance measurement process, wherein the estimating ofthe first location of the base station comprises estimating the firstlocation of the base station based on the median distance between thebase station and the respective devices.
 5. The method of claim 1,wherein a potential cell location of the cell is the second location ofthe cell or a recorded location of the cell, wherein the recordedlocation of the cell is received from a data source device, and whereinthe method further comprises: determining, by the system, whether thepotential cell location of the cell is a valid cell location of the cellbased on the second location of the cell, the recorded location of thecell, or the respective timing advance measurement data associated withthe respective devices, and based on a third group of threshold accuracyvalues.
 6. The method of claim 5, further comprising: tagging, by thesystem, the potential cell location of the cell as one of an accuratestatus, an acceptably accurate status, an inaccurate status, or anuncertain status, based on a determination result of determining whetherthe potential cell location of the cell is the valid location of thecell, wherein the third group of threshold accuracy values comprises afirst threshold accuracy value associated with the accurate status, asecond threshold accuracy value associated with the acceptably accuratestatus, and a third threshold accuracy value associated with theinaccurate status and the uncertain status.
 7. The method of claim 1,wherein a potential cell location of the cell is the second location ofthe cell or a recorded location of the cell, wherein the recordedlocation of the cell is received from a data source device, wherein therespective distances are respective first distances, and wherein themethod further comprises: determining, by the system, respective seconddistances between the potential cell location and the respective devicesbased on the respective location data; determining, by the system,respective upper bound values of error as a function of the respectivesecond distances and the respective timing advance measurement data; anddetermining, by the system, whether the respective upper bound values oferror satisfy a defined threshold upper bound value of error relating toa reliability of the recorded location of the cell or the secondlocation of the cell.
 8. The method of claim 7, further comprising: inresponse to determining that the respective upper bound values of errorsatisfy the defined threshold upper bound value of error, determining,by the system, that the recorded location of the cell or the secondlocation of the cell is to be flagged as good and tagged with anaccurate status.
 9. The method of claim 7, further comprising: inresponse to determining that one or more of the respective upper boundvalues of error do not satisfy the defined threshold upper bound valueof error, determining, by the system, respective third distances betweenthe potential cell location and the respective devices based on therespective timing advance measurement data; determining, by the system,respective distance differences between the respective second distancesand the respective third distances; and determining, by the system,whether the respective distance differences between the respectivesecond distances and the respective third distances satisfy a definedthreshold distance difference value.
 10. The method of claim 9, whereinthe defined threshold distance value is a first defined thresholddistance value, and wherein the method further comprises: in response todetermining that the respective distance differences satisfy the definedthreshold distance difference value, determining, by the system, adistance between the recorded location of the cell and the secondlocation of the cell; and determining, by the system, whether thedistance satisfies a second defined threshold distance value relating tothe reliability of the recorded location of the cell or the secondlocation of the cell.
 11. The method of claim 10, further comprising:one of: in response to determining that the distance satisfies thesecond defined threshold distance value, determining, by the system,that the recorded location of the cell or the second location of thecell is to be flagged as good and tagged with an accurate status, or inresponse to determining that the distance does not satisfy the seconddefined threshold distance value, determining, by the system, that therecorded location of the cell or the second location of the cell is tobe flagged as fine and tagged with an acceptably accurate status. 12.The method of claim 9, further comprising: in response to determiningthat the respective distance differences do not satisfy the definedthreshold distance difference value, determining, by the system, thatthe recorded location of the cell or the second location of the cell isto be flagged as bad or uncertain.
 13. The method of claim 12, furthercomprising: in response to determining that the respective distancedifferences do not satisfy the defined threshold distance differencevalue, determining, by the system, whether a number of devices of therespective devices satisfies a defined threshold number relating tosufficiency of cell location-related data; and one of: in response todetermining that the number of devices satisfies the defined thresholdnumber, determining, by the system, that the recorded location of thecell or the second location of the cell is to be flagged as bad andtagged with an inaccurate status, or in response to determining that thenumber of devices does not satisfy the defined threshold number,determining, by the system, that the recorded location of the cell orthe second location of the cell is to be flagged as uncertain and taggedwith an uncertain status.
 14. A system, comprising: a processor; and amemory that stores executable instructions that, when executed by theprocessor, facilitate performance of operations, comprising: analyzingrespective timing advance measurement information and respectivelocation information associated with respective devices of a first groupof devices, wherein the first group of devices is associated withnetwork equipment, and wherein the network equipment is associated witha second group of cells comprising a cell; estimating a first locationof the network equipment, based on a first result of analyzing therespective timing advance measurement information and the respectivelocation information, to facilitate estimating a second location of thecell; and determining an estimation procedure of a group of estimationprocedures to utilize to facilitate the estimating of the first locationof the network equipment, wherein the group of estimation processescomprises a linear regression analysis procedure and a disparateestimation procedure, wherein the determining of the estimationprocedure comprises determining whether to apply the linear regressionanalysis procedure to facilitate the estimating of the first location ofthe network equipment based on a second result of determining whetherrespective distances between the respective devices and the networkequipment satisfy a defined threshold distance value.
 15. The system ofclaim 14, wherein the different estimation process is a timing advancemeasurement procedure, and wherein the operations further comprise:based on a third result of analyzing the respective timing advancemeasurement information associated with the respective devices,determining whether the respective distances between the respectivedevices and the network equipment satisfy the defined threshold distancevalue, wherein the respective timing advance measurement informationindicates the respective distances between the respective devices andthe network equipment; and based on second result of determining whetherthe respective distances between the respective devices and the networkequipment satisfy the defined threshold distance value, determiningwhether to utilize the linear regression analysis procedure or thetiming advance measurement procedure to facilitate the estimating of thefirst location of the network equipment.
 16. The system of claim 15,wherein the operations further comprise: based on the second resultindicating that an insufficient number of the respective distancesbetween the respective devices and the network equipment satisfy thedefined threshold distance value, determining that the linear regressionanalysis procedure is to be utilized to facilitate the estimating of thefirst location of the network equipment; and performing the linearregression analysis procedure on the respective timing advancemeasurement information associated with the respective devices, whereinthe estimating of the first location of the network equipment comprisesestimating the first location of the network equipment based on a fourthresult of the linear regression analysis procedure.
 17. The system ofclaim 15, wherein the operations further comprise: based on the secondresult indicating that a sufficient number of the respective distancesbetween the respective devices and the network equipment satisfy thedefined threshold distance value, determining that the timing advancemeasurement procedure is to be utilized to facilitate the estimating ofthe first location of the network equipment; and determining a mediandistance between the network equipment and the respective devices basedon the respective location information, in accordance with the timingadvance measurement procedure, wherein the estimating of the firstlocation of the network equipment comprises estimating the firstlocation of the network equipment based on the median distance betweenthe network equipment and the respective devices.
 18. The system ofclaim 14, wherein a potential cell location of the cell is the secondlocation of the cell or a recorded location of the cell, wherein therecorded location of the cell is received from a data source device, andwherein the operations further comprise: determining whether thepotential cell location of the cell is a valid cell location of the cellbased on the second location of the cell, the recorded location of thecell, or the respective timing advance measurement informationassociated with the respective devices, and based on a third group ofthreshold accuracy values; and based on a determination result ofdetermining whether the potential cell location of the cell is the validlocation of the cell, tagging the potential cell location of the cell asone of an accurate status, an acceptably accurate status, an inaccuratestatus, or an uncertain status, wherein the third group of thresholdaccuracy values comprises a first threshold accuracy value associatedwith the accurate status, a second threshold accuracy value associatedwith the acceptably accurate status, and a third threshold accuracyvalue associated with the inaccurate status and the uncertain status.19. A non-transitory machine-readable medium, comprising executableinstructions that, when executed by a processor, facilitate performanceof operations, comprising: evaluating respective trace record data andrespective location data associated with respective devices that areassociated with a base station, wherein the base station is associatedwith a group of cells comprising a cell; estimating a first location ofthe base station, based on a result of evaluating the respective tracerecord data and the respective location data, to facilitate estimating asecond location of the cell; and determining an estimation process of agroup of estimation processes to utilize to facilitate the estimating ofthe first location of the base station, wherein the group of estimationprocesses comprises a linear regression analysis process and a timingadvance measurement process, wherein the determining of the estimationprocess comprises determining whether to employ the linear regressionanalysis process to facilitate the estimating of the first location ofthe base station based on a second result of determining whetherrespective distances between the respective devices and the base stationsatisfy a defined threshold distance value.
 20. The non-transitorymachine-readable medium of claim 19, wherein a potential cell locationof the cell is the second location of the cell or a recorded location ofthe cell, wherein the recorded location of the cell is received from arecorded cell location device, and wherein the operations furthercomprise: determining whether the potential cell location of the cell isa valid cell location of the cell based on the second location of thecell, the recorded location of the cell, or the respective timingadvance measurement data associated with the respective devices, andbased on a third group of threshold accuracy values; and based on adetermination result of determining whether the potential cell locationof the cell is the valid location of the cell, tagging the potentialcell location of the cell as one of an accurate status, an acceptablyaccurate status, an inaccurate status, or an uncertain status, whereinthe third group of threshold accuracy values comprises a first thresholdaccuracy value associated with the accurate status, a second thresholdaccuracy value associated with the acceptably accurate status, and athird threshold accuracy value associated with the inaccurate status andthe uncertain status.